Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10140132
Kana Nagai, Yihe Chen, M. Spenko, R. Henderson, B. Pervan
This paper examines the safety of LiDAR-based navigation for driverless vehicles and aims to reduce the risk of extracting information from undesired obstacles. We define the faults of a LiDAR navigation system, derive the integrity risk equation, and suggest landmark environments to reduce the risk of fault-free position error and data association faults. We also present a method to quantify feature extraction risk using reflective tape on desired landmarks to enhance the intensity of returned signals. The high-intensity returns are used in feature extraction decisions between obstacles and pre-defined landmarks using the Neyman-Pearson Lemma. Our experiments demonstrate that the probability of incorrect extraction is below 10−14, and the method is sufficient to ensure safety.
{"title":"Integrity with Extraction Faults in LiDAR-Based Urban Navigation for Driverless Vehicles","authors":"Kana Nagai, Yihe Chen, M. Spenko, R. Henderson, B. Pervan","doi":"10.1109/PLANS53410.2023.10140132","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140132","url":null,"abstract":"This paper examines the safety of LiDAR-based navigation for driverless vehicles and aims to reduce the risk of extracting information from undesired obstacles. We define the faults of a LiDAR navigation system, derive the integrity risk equation, and suggest landmark environments to reduce the risk of fault-free position error and data association faults. We also present a method to quantify feature extraction risk using reflective tape on desired landmarks to enhance the intensity of returned signals. The high-intensity returns are used in feature extraction decisions between obstacles and pre-defined landmarks using the Neyman-Pearson Lemma. Our experiments demonstrate that the probability of incorrect extraction is below 10−14, and the method is sufficient to ensure safety.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133441144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10140065
Taehoon Lee, Byungjin Lee, Jae-Ryong Yun, S. Sung
In this paper, we propose a method to integrate data from Inertial Navigation System (INS), Magnetic Pose Estimation System (MPS), and Laser Imaging Detection and Ranging (LiDAR) using a Federated Kalman Filter (FKF). We adaptively adjusted the information sharing factor using the Mahalanobis distance to maintain navigation performance in indoor environments with mirrors that contaminate LiDAR measurements. By adaptively adjusting the information sharing factor, we can adjust the weight of each local filter. To validate navigation performance, we conducted UGV driving tests in various indoor environments. We conducted experiments by driving a UGV on a course with a diameter of 3.6 meters. UGVs are equipped with LiDAR, MPS receivers, and IMUs to measure data. We used four 1-meter diameter MPS coils. An optical motion capture device, the Optitrack, was used as reference data.
{"title":"INS/MPS/LiDAR Integrated Navigation System Using Federated Kalman Filter in an Indoor Environment","authors":"Taehoon Lee, Byungjin Lee, Jae-Ryong Yun, S. Sung","doi":"10.1109/PLANS53410.2023.10140065","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140065","url":null,"abstract":"In this paper, we propose a method to integrate data from Inertial Navigation System (INS), Magnetic Pose Estimation System (MPS), and Laser Imaging Detection and Ranging (LiDAR) using a Federated Kalman Filter (FKF). We adaptively adjusted the information sharing factor using the Mahalanobis distance to maintain navigation performance in indoor environments with mirrors that contaminate LiDAR measurements. By adaptively adjusting the information sharing factor, we can adjust the weight of each local filter. To validate navigation performance, we conducted UGV driving tests in various indoor environments. We conducted experiments by driving a UGV on a course with a diameter of 3.6 meters. UGVs are equipped with LiDAR, MPS receivers, and IMUs to measure data. We used four 1-meter diameter MPS coils. An optical motion capture device, the Optitrack, was used as reference data.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133159153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10140072
R. R. Khan, A. Hanif, Q. Ahmed
This paper focuses on threat and vulnerability analysis using a cooperative navigation strategy for highly automated vehicles operating at smart intersections. This work considers highly automated vehicles (HAVs) to operate simultaneously with connected but non-cooperative vehicles. The proposed work uses the beyond visual range information to reduce vulnerable situations. The safety of Vulnerable road users and the framework of Cooperative navigation is accomplished by using the data from the Road-Side Units (RSU) and On-board Units (OBU). Signalized intersection scenario uses information from the RSU, OBU, Autonomous Intersection Management (AIM) system, and Smart Traffic Lights (STL). This work presents the attack trees of the sensors used in automotive industries to calculate Position, Navigation, and Timing (PNT) solutions. This paper also presents systems Failure Mode and Effect Analysis (FMEA) to see the hazards related to the attack on the sensor, its effect on the subsystems, and the PNT solutions outcome. Threats and vulnerabilities are further validated by the design and test of the cooperative navigation algorithm and their quantitative results. Safety results are also used to generate the Threat Assessment and Risk Analysis (TARA) matrix for quantities analysis. The presented threat and vulnerability analysis are the near future requirement where the vehicle depends on onboard sensors and utilizes information from infrastructure devices. Jamming of infrastructure devices and interference into the OBU is enforced to evaluate the cooperative navigation framework in vulnerable situations occurring at the intersection. The results presented in this work will help enhance safety at smart intersections and drive attention toward more fatal scenarios. A literature survey was conducted to generate the relationship between the sensors and the subsystem shown in figure 2. Further analyses were done to develop the link between vulnerabilities and threats associated with sensors, shown in figure 3. Threats and vulnerabilities on cooperative autonomous driving system risk analysis through Attack trees that were developed based on literature review. Figure 4 to 9 shows the attack tree that defines the sensors' vulnerabilities that lead to threats. Figure 10 shows the FMEA of HAVs that established the link between sensors with the subsystem. Since errors generated in each subsystem will lead to errors in PNT solutions, Therefore figure 10 shows the link between the affected PNT solution with threats associated with the faulty solution. To enhance safety, a cooperative navigation framework is used to validate the scenario and threat risk analysis based on the literature review in relation to subsystems, sensors, threats, and vulnerabilities as mentioned in figures 2 and 3. Multiple threat scenarios were simulated and results of separation between ego vehicle and actor vehicles were presented in figures 12, 13, and 14. Figures 12, 13, and 14 show the
本文重点研究了在智能交叉口运行的高度自动驾驶车辆的协同导航策略的威胁和漏洞分析。这项工作考虑高度自动化车辆(hav)与连接但非合作的车辆同时运行。该方法利用超视距信息来减少易受攻击的情况。利用路侧单元(road - side Units, RSU)和车载单元(On-board Units, OBU)的数据，实现弱势道路使用者的安全与协同导航框架。信号交叉口场景使用来自RSU、OBU、自治交叉口管理(AIM)系统和智能交通灯(STL)的信息。这项工作提出了汽车工业中用于计算位置、导航和定时(PNT)解决方案的传感器的攻击树。本文还介绍了系统故障模式和影响分析(FMEA)，以查看与传感器攻击相关的危害，其对子系统的影响以及PNT解决方案的结果。通过对协同导航算法的设计、测试和量化结果，进一步验证了威胁和漏洞。安全结果还用于生成威胁评估和风险分析(TARA)矩阵，用于数量分析。提出的威胁和漏洞分析是车辆依赖车载传感器和利用基础设施设备信息的近期需求。通过对基础设施设备的干扰和对OBU的干扰来评估十字路口脆弱情况下的协同导航框架。这项工作的结果将有助于提高智能十字路口的安全性，并将注意力转向更致命的场景。通过文献调查，得出了传感器与子系统之间的关系，如图2所示。进一步的分析开发了与传感器相关的漏洞和威胁之间的联系，如图3所示。在文献综述的基础上，提出了基于攻击树的协作式自动驾驶系统风险分析的威胁与漏洞。图4 ~ 9所示的攻击树定义了导致威胁的传感器漏洞。图10显示了在传感器与子系统之间建立链接的hav的FMEA。由于每个子系统中生成的错误将导致PNT解决方案中的错误，因此图10显示了受影响的PNT解决方案与与错误解决方案相关的威胁之间的链接。为了提高安全性，基于图2和图3所示的与子系统、传感器、威胁和漏洞相关的文献综述，使用协作导航框架来验证场景和威胁风险分析。模拟多个威胁场景，图12、图13、图14给出了自我车辆与行为车辆分离的结果。图12、图13、图14显示的是间隔时间，最小允许安全间隔为2秒，间隔时间小于2秒的车辆将处于脆弱状态。表1用红色、绿色和黄色三种不同的颜色显示了严重性级别。红色单元格表示车辆在最脆弱的情况下运行。这项工作提出了联网自动驾驶汽车的威胁和漏洞，并验证了与每个子系统相关的风险。为了进一步提高安全性，这项工作也可以扩展到其他子系统，因为只有路径跟踪和避碰结果得到了验证。这一分析将增强并有助于在智能十字路口运行的联网自动驾驶汽车的安全性。今后可以对交叉口的动态情景进行分析，以提高交叉口的安全性。
{"title":"Threat Analysis of Position, Navigation, and Timing for Highly Automated Vehicles","authors":"R. R. Khan, A. Hanif, Q. Ahmed","doi":"10.1109/PLANS53410.2023.10140072","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140072","url":null,"abstract":"This paper focuses on threat and vulnerability analysis using a cooperative navigation strategy for highly automated vehicles operating at smart intersections. This work considers highly automated vehicles (HAVs) to operate simultaneously with connected but non-cooperative vehicles. The proposed work uses the beyond visual range information to reduce vulnerable situations. The safety of Vulnerable road users and the framework of Cooperative navigation is accomplished by using the data from the Road-Side Units (RSU) and On-board Units (OBU). Signalized intersection scenario uses information from the RSU, OBU, Autonomous Intersection Management (AIM) system, and Smart Traffic Lights (STL). This work presents the attack trees of the sensors used in automotive industries to calculate Position, Navigation, and Timing (PNT) solutions. This paper also presents systems Failure Mode and Effect Analysis (FMEA) to see the hazards related to the attack on the sensor, its effect on the subsystems, and the PNT solutions outcome. Threats and vulnerabilities are further validated by the design and test of the cooperative navigation algorithm and their quantitative results. Safety results are also used to generate the Threat Assessment and Risk Analysis (TARA) matrix for quantities analysis. The presented threat and vulnerability analysis are the near future requirement where the vehicle depends on onboard sensors and utilizes information from infrastructure devices. Jamming of infrastructure devices and interference into the OBU is enforced to evaluate the cooperative navigation framework in vulnerable situations occurring at the intersection. The results presented in this work will help enhance safety at smart intersections and drive attention toward more fatal scenarios. A literature survey was conducted to generate the relationship between the sensors and the subsystem shown in figure 2. Further analyses were done to develop the link between vulnerabilities and threats associated with sensors, shown in figure 3. Threats and vulnerabilities on cooperative autonomous driving system risk analysis through Attack trees that were developed based on literature review. Figure 4 to 9 shows the attack tree that defines the sensors' vulnerabilities that lead to threats. Figure 10 shows the FMEA of HAVs that established the link between sensors with the subsystem. Since errors generated in each subsystem will lead to errors in PNT solutions, Therefore figure 10 shows the link between the affected PNT solution with threats associated with the faulty solution. To enhance safety, a cooperative navigation framework is used to validate the scenario and threat risk analysis based on the literature review in relation to subsystems, sensors, threats, and vulnerabilities as mentioned in figures 2 and 3. Multiple threat scenarios were simulated and results of separation between ego vehicle and actor vehicles were presented in figures 12, 13, and 14. Figures 12, 13, and 14 show the","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123695828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10140024
Alejandro Gonzalez-Garrido, J. Querol, S. Chatzinotas
The Fifth Generation (5G) New Radio offers a new Positioning, Navigation, and Timing (PNT) service with larger signal bandwidth and higher frequency carriers than previous generations, delivering more accurate measurements. This allows other vertical industries to benefit from this feature, opening up new possibilities. Furthermore, the 5G network includes Non-Terrestrial Network (NTN) elements such as Unmanned Aerial Vehicle (UAV), High-Altitude Platform Systems (HAPS), and satellites, which are gaining significant attention from the industry to allow for global communication. The future 6G aims to create a single network entity with multiple connectivity layers for all devices in all scenarios. Therefore, when combining both aspects of the 5G networks, the PNT service, and the NTN, there are several benefits such as: an independent and complete communication and navigation system under a single network, higher accuracy on the PNT solution than previous generation, global coverage for join navigation and communication, higher resilience on the positioning estimation, or new services offered. However, this is not free of challenges, as it is expected to achieve an accuracy, at least, similar to Global Navigation Satellite System (GNSS). One of the challenges is the multiplexing of the data and positioning service using a single infrastructure such a satellite. This paper has the purpose of analysing the effect in the accuracy of a delay estimator when a satellite constellation send a Positioning Reference Signal (PRS). Assuming that all satellites share the same frequency carrier and are synchronised between them. This 5G PRS main characteristic is its flexibility in terms of resource usage such as bandwidth, resource element density, symbols periodicity, a muting scheme, etc. This flexibility will be exploited in this paper to get a UE capable to estimate the Downlink Observed Time Difference of Arrival (DL-OTDoA) of the signal. Two challenges are present in this work, both are related to the characteristics of the RF channel between the Next Generation Base Station (gNB) and the User Equipment (UE): the first one is how the UE will cope with the high Doppler shift due to the high speed of the Low Earth Orbit (LEO) gNB increasing the Inter-Carrier Interference (ICI); and the second challenge is the effect of variable delay between OFDM symbols in the same slot and transmitter, increasing the effect of Intersymbol Interference (ISI). The contribution of the authors on this paper is the analysis of different PRS configuration that keeps a low interfere level between the moving gNBs. The result of this research highlight the impact that the length in number of subcarriers and number of OFDM symbol has in the accuracy of the delay estimation. It shows a trade-off in the constellation design, as a higher number of satellites in visibility also increase the ICI and ISI.
{"title":"5G Positioning Reference Signal Configuration for Integrated Terrestrial/Non-Terrestrial Network Scenario","authors":"Alejandro Gonzalez-Garrido, J. Querol, S. Chatzinotas","doi":"10.1109/PLANS53410.2023.10140024","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140024","url":null,"abstract":"The Fifth Generation (5G) New Radio offers a new Positioning, Navigation, and Timing (PNT) service with larger signal bandwidth and higher frequency carriers than previous generations, delivering more accurate measurements. This allows other vertical industries to benefit from this feature, opening up new possibilities. Furthermore, the 5G network includes Non-Terrestrial Network (NTN) elements such as Unmanned Aerial Vehicle (UAV), High-Altitude Platform Systems (HAPS), and satellites, which are gaining significant attention from the industry to allow for global communication. The future 6G aims to create a single network entity with multiple connectivity layers for all devices in all scenarios. Therefore, when combining both aspects of the 5G networks, the PNT service, and the NTN, there are several benefits such as: an independent and complete communication and navigation system under a single network, higher accuracy on the PNT solution than previous generation, global coverage for join navigation and communication, higher resilience on the positioning estimation, or new services offered. However, this is not free of challenges, as it is expected to achieve an accuracy, at least, similar to Global Navigation Satellite System (GNSS). One of the challenges is the multiplexing of the data and positioning service using a single infrastructure such a satellite. This paper has the purpose of analysing the effect in the accuracy of a delay estimator when a satellite constellation send a Positioning Reference Signal (PRS). Assuming that all satellites share the same frequency carrier and are synchronised between them. This 5G PRS main characteristic is its flexibility in terms of resource usage such as bandwidth, resource element density, symbols periodicity, a muting scheme, etc. This flexibility will be exploited in this paper to get a UE capable to estimate the Downlink Observed Time Difference of Arrival (DL-OTDoA) of the signal. Two challenges are present in this work, both are related to the characteristics of the RF channel between the Next Generation Base Station (gNB) and the User Equipment (UE): the first one is how the UE will cope with the high Doppler shift due to the high speed of the Low Earth Orbit (LEO) gNB increasing the Inter-Carrier Interference (ICI); and the second challenge is the effect of variable delay between OFDM symbols in the same slot and transmitter, increasing the effect of Intersymbol Interference (ISI). The contribution of the authors on this paper is the analysis of different PRS configuration that keeps a low interfere level between the moving gNBs. The result of this research highlight the impact that the length in number of subcarriers and number of OFDM symbol has in the accuracy of the delay estimation. It shows a trade-off in the constellation design, as a higher number of satellites in visibility also increase the ICI and ISI.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123861968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, smartphones are the first choice for vehicle navigation. Due to the low quality of its embedded Inertial Measurement Unit (IMU), some self-constrained technologies have been developed to reduce the divergence of error in GNSS-denied areas, such as Zero Velocity Update (ZUPT) and Non-Holonomic Constraints (NHC). Their rear wheels are considered as the active position of NHC, while smartphones are usually installed on a holder in the front of the vehicle to guide drivers. To ensure the effectiveness of NHC, there is an urgent need to calibrate the lever arms and the installation angles between the smartphone and the previously mentioned active position. The lever arm is relatively stable under most situations since the position of the phone holder in the vehicle is usually fixed, which can be measured by a tape measure or estimated by the parameters of the car directly. The installation angle is difficult to be accurately measured and it may change every time we install the smartphone into the holder. Excluding the roll angle that does not affect the validity of NHC, an automatic estimation algorithm of the pitch and heading installation angles is needed. In this paper, we proposed a deep learning-driven automatic estimation of smartphone installation angles to enhance the performance of smartphone-based vehicle navigation in GNSS-denied areas. In the first step, an Extended Kalman Filter (EKF) is used to integrate GNSS/IMU/Barometer/DeepOdometry to provide accurate positions and attitudes. Simultaneously, the data of IMU and barometer are input into the trained deep learning network to output the predicted positions with the attitudes obtained from the integrated system. Then, the installation angles are estimated as states in another EKF by differing the predicted positions and the integrated positions. Extensive experiments show that our proposed method can estimate pitch and heading installation angles in deviation within 1 degree.
{"title":"Deep Learning-driven Automatic Estimation of Smartphone Installation Angles for Vehicle Navigation","authors":"Jingxian Wang, Weihao Ding, Bingbo Cui, Jianbo Shao, D. Weng, Wu Chen","doi":"10.1109/PLANS53410.2023.10140107","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140107","url":null,"abstract":"Currently, smartphones are the first choice for vehicle navigation. Due to the low quality of its embedded Inertial Measurement Unit (IMU), some self-constrained technologies have been developed to reduce the divergence of error in GNSS-denied areas, such as Zero Velocity Update (ZUPT) and Non-Holonomic Constraints (NHC). Their rear wheels are considered as the active position of NHC, while smartphones are usually installed on a holder in the front of the vehicle to guide drivers. To ensure the effectiveness of NHC, there is an urgent need to calibrate the lever arms and the installation angles between the smartphone and the previously mentioned active position. The lever arm is relatively stable under most situations since the position of the phone holder in the vehicle is usually fixed, which can be measured by a tape measure or estimated by the parameters of the car directly. The installation angle is difficult to be accurately measured and it may change every time we install the smartphone into the holder. Excluding the roll angle that does not affect the validity of NHC, an automatic estimation algorithm of the pitch and heading installation angles is needed. In this paper, we proposed a deep learning-driven automatic estimation of smartphone installation angles to enhance the performance of smartphone-based vehicle navigation in GNSS-denied areas. In the first step, an Extended Kalman Filter (EKF) is used to integrate GNSS/IMU/Barometer/DeepOdometry to provide accurate positions and attitudes. Simultaneously, the data of IMU and barometer are input into the trained deep learning network to output the predicted positions with the attitudes obtained from the integrated system. Then, the installation angles are estimated as states in another EKF by differing the predicted positions and the integrated positions. Extensive experiments show that our proposed method can estimate pitch and heading installation angles in deviation within 1 degree.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114431678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10139935
Helena Calatrava, D. Medina, P. Closas
Global Navigation Satellite Systems (GNSS) is a popular positioning solution able to provide high accuracy, integrity, reliability and high coverage. GNSS performance may be enhanced through aiding systems such as Differential GNSS (DGNSS), which aims to mitigate disruptive sources of error by using corrections sent from a reference station. In this paper, we investigate a method that provides performance results comparable to those by DGNSS without the need for a reference station. We propose the Massive User-Centric Single Difference (MUCSD) algorithm, which leverages a set of collaborative receivers exchanging observables and, potentially, their noisy estimates of position and clock bias. MUCSD is implemented as an iterative weighted least squares (WLS) estimator and its lower accuracy bound, as given by the Cramér-Rao Bound (CRB), is derived as a performance benchmark for the WLS solution. Simulation results are provided as a function of the number of collaborative users and the exchanged information uncertainty. Results show that, without having to access costly-to-maintain reference stations, MUCSD asymptotically outperforms DGNSS as the number of collaborative receivers grows.
{"title":"Massive Differencing of GNSS Pseudorange Measurements","authors":"Helena Calatrava, D. Medina, P. Closas","doi":"10.1109/PLANS53410.2023.10139935","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10139935","url":null,"abstract":"Global Navigation Satellite Systems (GNSS) is a popular positioning solution able to provide high accuracy, integrity, reliability and high coverage. GNSS performance may be enhanced through aiding systems such as Differential GNSS (DGNSS), which aims to mitigate disruptive sources of error by using corrections sent from a reference station. In this paper, we investigate a method that provides performance results comparable to those by DGNSS without the need for a reference station. We propose the Massive User-Centric Single Difference (MUCSD) algorithm, which leverages a set of collaborative receivers exchanging observables and, potentially, their noisy estimates of position and clock bias. MUCSD is implemented as an iterative weighted least squares (WLS) estimator and its lower accuracy bound, as given by the Cramér-Rao Bound (CRB), is derived as a performance benchmark for the WLS solution. Simulation results are provided as a function of the number of collaborative users and the exchanged information uncertainty. Results show that, without having to access costly-to-maintain reference stations, MUCSD asymptotically outperforms DGNSS as the number of collaborative receivers grows.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121285388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10140108
J. Jung, Chan Gook Park
In this paper, we present a framework of simultaneous localization and mapping (SLAM) by combining the modular visual-inertial odometry (VIO) and object SLAM estimator. Semantic objects are known to possess rich localization information, such as scale and orientation. However, how to tightly couple these object measurements to an inertial sensor is not straightforward. To answer this, we fuse local object poses from a deep neural network to build a globally consistent object map under precise prior estimates from the VIO module. The contribution of our work is the representation of the object map with six-dimensional poses that enables a robot to exploit orientational, as well as positional information in the filtering formulation. We showcase that our method can output cm-level accuracy localization and mapping in a room-scale environment in our photo-realistic virtual environment.
{"title":"A Framework for Visual-Inertial Object-Level Simultaneous Localization and Mapping","authors":"J. Jung, Chan Gook Park","doi":"10.1109/PLANS53410.2023.10140108","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140108","url":null,"abstract":"In this paper, we present a framework of simultaneous localization and mapping (SLAM) by combining the modular visual-inertial odometry (VIO) and object SLAM estimator. Semantic objects are known to possess rich localization information, such as scale and orientation. However, how to tightly couple these object measurements to an inertial sensor is not straightforward. To answer this, we fuse local object poses from a deep neural network to build a globally consistent object map under precise prior estimates from the VIO module. The contribution of our work is the representation of the object map with six-dimensional poses that enables a robot to exploit orientational, as well as positional information in the filtering formulation. We showcase that our method can output cm-level accuracy localization and mapping in a room-scale environment in our photo-realistic virtual environment.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116724731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10140123
Tomihisa Welsh, Sean M. Marks, Alex Pronschinske
Vehicle localization and navigation in a GPS-denied or GPS-degraded environment is a common use case in both civilian and military applications. Augmented reality (AR) applications in particular require a high level of localization accuracy to be perceptually convincing. In this paper we discuss our experimental results implementing a complete, working navigation system for vehicular AR, which is able to maintain high localization accuracy in situations where GPS loss occurs for significant periods of time. We have implemented a hybrid state filter that is able to considerably improve GPS-denied dead-reckoning solutions by merging the output of an Unscented Kalman Filter (UKF), or any off the shelf pose solution with our map-corrected particle filter. The solution is initialized with a known starting location and subsequently corrects the GPS-denied pose solution by performing a “road-aiding” correction using a distance-transform metric derived from an OpenStreetMaps (OSM) map. A calibrated camera provides RGB input to a semantic segmentation network that determines the location of the road. The geometry of the labelling helps the system decide whether the vehicle is on or off road and subsequently whether the map correction can be applied. Our experimental results show a marked improvement in overall accuracy under GPS-denied conditions over a purely dead-reckoning INS solution on a truck mounted system on public roads. To demonstrate the robustness of our system, we drove for 112 minutes GPS-denied, achieving a median positional error of 5 meters and a median heading error of 28 mrad. This degree of accuracy supported consistent and perceptually convincing AR.
{"title":"GPS-denied Vehicle Localization for Augmented Reality Using a Road-Aided Particle Filter and RGB Camera","authors":"Tomihisa Welsh, Sean M. Marks, Alex Pronschinske","doi":"10.1109/PLANS53410.2023.10140123","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140123","url":null,"abstract":"Vehicle localization and navigation in a GPS-denied or GPS-degraded environment is a common use case in both civilian and military applications. Augmented reality (AR) applications in particular require a high level of localization accuracy to be perceptually convincing. In this paper we discuss our experimental results implementing a complete, working navigation system for vehicular AR, which is able to maintain high localization accuracy in situations where GPS loss occurs for significant periods of time. We have implemented a hybrid state filter that is able to considerably improve GPS-denied dead-reckoning solutions by merging the output of an Unscented Kalman Filter (UKF), or any off the shelf pose solution with our map-corrected particle filter. The solution is initialized with a known starting location and subsequently corrects the GPS-denied pose solution by performing a “road-aiding” correction using a distance-transform metric derived from an OpenStreetMaps (OSM) map. A calibrated camera provides RGB input to a semantic segmentation network that determines the location of the road. The geometry of the labelling helps the system decide whether the vehicle is on or off road and subsequently whether the map correction can be applied. Our experimental results show a marked improvement in overall accuracy under GPS-denied conditions over a purely dead-reckoning INS solution on a truck mounted system on public roads. To demonstrate the robustness of our system, we drove for 112 minutes GPS-denied, achieving a median positional error of 5 meters and a median heading error of 28 mrad. This degree of accuracy supported consistent and perceptually convincing AR.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116614527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10140118
F. Prol, S. Kaasalainen, E. Lohan, M. Z. H. Bhuiyan, J. Praks, H. Kuusniemi
As the whole space segment of satellites in low Earth orbits (LEO) grows, simulations of positioning, navigation, and timing (PNT) through LEO satellites are needed to understand the possible gains that the upcoming satellite missions can offer to global navigation satellite systems (GNSS). The simulations do not only help to forecast the optimal GNSS future advancements, but also guide us on how to implement the most optimized PNT missions. In the most recent years, several simulation tools have focused on broadcast orbit models, precise orbit determination of LEO satellites, signal structure designs, atmospheric models, constellation optimization strategies, satellite clock implementations, and positioning integration with distinct sensors. In this work, we overview most of the latest developments found in the literature to define the status and challenges of LEO- PNT system simulations.
{"title":"Simulations using LEO-PNT systems: A Brief Survey","authors":"F. Prol, S. Kaasalainen, E. Lohan, M. Z. H. Bhuiyan, J. Praks, H. Kuusniemi","doi":"10.1109/PLANS53410.2023.10140118","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140118","url":null,"abstract":"As the whole space segment of satellites in low Earth orbits (LEO) grows, simulations of positioning, navigation, and timing (PNT) through LEO satellites are needed to understand the possible gains that the upcoming satellite missions can offer to global navigation satellite systems (GNSS). The simulations do not only help to forecast the optimal GNSS future advancements, but also guide us on how to implement the most optimized PNT missions. In the most recent years, several simulation tools have focused on broadcast orbit models, precise orbit determination of LEO satellites, signal structure designs, atmospheric models, constellation optimization strategies, satellite clock implementations, and positioning integration with distinct sensors. In this work, we overview most of the latest developments found in the literature to define the status and challenges of LEO- PNT system simulations.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"23 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114017412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1109/PLANS53410.2023.10140016
Christian Siebert, A. Konovaltsev, M. Meurer
Multipath propagation is still a major source of error in global navigation satellite systems (GNSSs), especially in urban environments. Conventional GNSS receivers provide under such conditions only a degraded accuracy. At the same time, applying an effective but computationally complex multipath mitigation algorithm potentially exceeds cost or energy consumption requirements. Therefore, a low-complexity multipath mitigation technique is proposed in this paper. It relies on a multi-correlator structure with an Extended Kalman Filter (EKF) replacing the conventional delay locked loop (DLL) for the code tracking. Multipath resilience is achieved by incorporating the radio propagation channel between satellite and user in the measurement model, inherently accounting for reflected signal replicas. In order to reduce complexity, the effect of the number and distribution of the correlators used has been investigated. It turned out, that even with a very low number of correlators, a high multipath mitigation capability is maintained. The results have been validated with actual measurement data.
{"title":"Low-Complexity Multipath Mitigation Technique Based on Multi-Correlator Structures","authors":"Christian Siebert, A. Konovaltsev, M. Meurer","doi":"10.1109/PLANS53410.2023.10140016","DOIUrl":"https://doi.org/10.1109/PLANS53410.2023.10140016","url":null,"abstract":"Multipath propagation is still a major source of error in global navigation satellite systems (GNSSs), especially in urban environments. Conventional GNSS receivers provide under such conditions only a degraded accuracy. At the same time, applying an effective but computationally complex multipath mitigation algorithm potentially exceeds cost or energy consumption requirements. Therefore, a low-complexity multipath mitigation technique is proposed in this paper. It relies on a multi-correlator structure with an Extended Kalman Filter (EKF) replacing the conventional delay locked loop (DLL) for the code tracking. Multipath resilience is achieved by incorporating the radio propagation channel between satellite and user in the measurement model, inherently accounting for reflected signal replicas. In order to reduce complexity, the effect of the number and distribution of the correlators used has been investigated. It turned out, that even with a very low number of correlators, a high multipath mitigation capability is maintained. The results have been validated with actual measurement data.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116657717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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