Pub Date : 2012-04-23DOI: 10.1109/PLANS.2012.6236949
B. Schnaufer, P. Hwang, J. Nadke, G. McGraw, D. Venable
Accurate and robust position, navigation, and time (PNT) data is a key-enabler for multi-platform collaborative sensing for a diverse array of military operations. The AFRL Collaborative Robust Integrated Sensor Positioning (CRISP) program has investigated techniques that leverage shared sensor data to enhance the availability of reliable PNT information for all platforms in a network, particularly those that experience GPS outages, utilizing electro-optical (EO) payload data in conjunction with GPS and inertial data. Rockwell Collins has developed an architecture for enabling continuous navigation capabilities for airborne platforms having disparate sensing capabilities in GPS challenged and denied conditions. In this study program the Rockwell Collins architecture was evaluated via a high-fidelity simulation environment. Navigation performance results for our solution were produced and presented by exercising our simulation with an AFRL-provided dataset. The performance results have shown that our collaborative image navigation architecture and implementation supports extended, GPS-level navigation accuracy in GPS Denied conditions.
{"title":"Collaborative image navigation simulation and analysis for UAVs in GPS challenged conditions","authors":"B. Schnaufer, P. Hwang, J. Nadke, G. McGraw, D. Venable","doi":"10.1109/PLANS.2012.6236949","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236949","url":null,"abstract":"Accurate and robust position, navigation, and time (PNT) data is a key-enabler for multi-platform collaborative sensing for a diverse array of military operations. The AFRL Collaborative Robust Integrated Sensor Positioning (CRISP) program has investigated techniques that leverage shared sensor data to enhance the availability of reliable PNT information for all platforms in a network, particularly those that experience GPS outages, utilizing electro-optical (EO) payload data in conjunction with GPS and inertial data. Rockwell Collins has developed an architecture for enabling continuous navigation capabilities for airborne platforms having disparate sensing capabilities in GPS challenged and denied conditions. In this study program the Rockwell Collins architecture was evaluated via a high-fidelity simulation environment. Navigation performance results for our solution were produced and presented by exercising our simulation with an AFRL-provided dataset. The performance results have shown that our collaborative image navigation architecture and implementation supports extended, GPS-level navigation accuracy in GPS Denied conditions.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128217827","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 : 2012-04-23DOI: 10.1109/PLANS.2012.6236888
A. Trusov, I. Prikhodko, S. Zotov, A. Shkel
We report high-Q and wide dynamic range MEMS gyroscopes and accelerometers for development of an IMU capable of north finding and tracking. The vacuum sealed SOI gyroscope utilizes symmetric quadruple mass architecture with measured quality factors of 1.2 million and proven sub-°/hr Allan deviation of bias. The true north detection was accomplished in conventional amplitude modulated (AM) rate mode and showed 3 milliradian measurement uncertainty. The north (azimuth) tracking necessitates a wide dynamic range, for which the same QMG transducer is switched to a frequency modulated (FM) modality. The test results for FM operation experimentally demonstrated a wide linear input rate range of 18,000 °/s and inherent self-calibration against temperature changes. Vertical alignment is possible using resonant accelerometers with a projected bias error of 5 μg and self-calibration against temperature variations, enabled by differential frequency measurements. We believe the developed low dissipation inertial MEMS with interchangeable AM/FM modalities may enable wide dynamic range IMUs for north-finding and inertial guidance applications previously limited to optical and quartz systems.
{"title":"High-Q and wide dynamic range inertial MEMS for north-finding and tracking applications","authors":"A. Trusov, I. Prikhodko, S. Zotov, A. Shkel","doi":"10.1109/PLANS.2012.6236888","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236888","url":null,"abstract":"We report high-Q and wide dynamic range MEMS gyroscopes and accelerometers for development of an IMU capable of north finding and tracking. The vacuum sealed SOI gyroscope utilizes symmetric quadruple mass architecture with measured quality factors of 1.2 million and proven sub-°/hr Allan deviation of bias. The true north detection was accomplished in conventional amplitude modulated (AM) rate mode and showed 3 milliradian measurement uncertainty. The north (azimuth) tracking necessitates a wide dynamic range, for which the same QMG transducer is switched to a frequency modulated (FM) modality. The test results for FM operation experimentally demonstrated a wide linear input rate range of 18,000 °/s and inherent self-calibration against temperature changes. Vertical alignment is possible using resonant accelerometers with a projected bias error of 5 μg and self-calibration against temperature variations, enabled by differential frequency measurements. We believe the developed low dissipation inertial MEMS with interchangeable AM/FM modalities may enable wide dynamic range IMUs for north-finding and inertial guidance applications previously limited to optical and quartz systems.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133677240","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 : 2012-04-23DOI: 10.1109/PLANS.2012.6236827
Y. Stebler, S. Guerrier, Jan Skalud, Maria-Pia Victoria-Feser
Modeling and estimation of gyroscope and accelerometer errors is generally a very challenging task, especially for low-cost inertial MEMS sensors whose systematic errors have complex spectral structures. Consequently, identifying correct error-state parameters in a INS/GNSS Kalman filter/smoother becomes difficult when several processes are superimposed. In such situations, the classical identification approach via Allan Variance (AV) analyses fails due to the difficulty of separating the error-processes in the spectral domain. For this purpose we propose applying a recently developed estimation method, called the Generalized Method of Wavelet Moments (GMWM), that is excepted from such inconveniences. This method uses indirect inference on the parameters using the wavelet variances associated to the observed process. In this article, the GMWM estimator is applied in the context of modeling the behavior of low-cost inertial sensors. Its capability to estimate the parameters of models such as mixtures of GM processes for which no other estimation method succeeds is first demonstrated through simulation studies. The GMWM estimator is also applied on signals issued from a MEMS-based inertial measurement unit, using sums of GM processes as stochastic models. Finally, the benefits of using such models is highlighted by analyzing the quality of the determined trajectory provided by the INS/GNSS Kalman filter, in which artificial GNSS gaps were introduced. During these epochs, inertial navigation operates in coasting mode while GNSS-supported trajectory acts as a reference. As the overall performance of inertial navigation is strongly dependent on the errors corrupting its observations, the benefits of using the more appropriate error models (with respect to simpler ones estimated using classical AV graphical identification technique) are demonstrated by a significant improvement in the trajectory accuracy.
{"title":"A framework for inertial sensor calibration using complex stochastic error models","authors":"Y. Stebler, S. Guerrier, Jan Skalud, Maria-Pia Victoria-Feser","doi":"10.1109/PLANS.2012.6236827","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236827","url":null,"abstract":"Modeling and estimation of gyroscope and accelerometer errors is generally a very challenging task, especially for low-cost inertial MEMS sensors whose systematic errors have complex spectral structures. Consequently, identifying correct error-state parameters in a INS/GNSS Kalman filter/smoother becomes difficult when several processes are superimposed. In such situations, the classical identification approach via Allan Variance (AV) analyses fails due to the difficulty of separating the error-processes in the spectral domain. For this purpose we propose applying a recently developed estimation method, called the Generalized Method of Wavelet Moments (GMWM), that is excepted from such inconveniences. This method uses indirect inference on the parameters using the wavelet variances associated to the observed process. In this article, the GMWM estimator is applied in the context of modeling the behavior of low-cost inertial sensors. Its capability to estimate the parameters of models such as mixtures of GM processes for which no other estimation method succeeds is first demonstrated through simulation studies. The GMWM estimator is also applied on signals issued from a MEMS-based inertial measurement unit, using sums of GM processes as stochastic models. Finally, the benefits of using such models is highlighted by analyzing the quality of the determined trajectory provided by the INS/GNSS Kalman filter, in which artificial GNSS gaps were introduced. During these epochs, inertial navigation operates in coasting mode while GNSS-supported trajectory acts as a reference. As the overall performance of inertial navigation is strongly dependent on the errors corrupting its observations, the benefits of using the more appropriate error models (with respect to simpler ones estimated using classical AV graphical identification technique) are demonstrated by a significant improvement in the trajectory accuracy.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133434318","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 : 2012-04-23DOI: 10.1109/PLANS.2012.6236836
M. Tahir, L. Presti, M. Fantino
The problem of designing the robust architectures for tracking the global navigation satellite system (GNSS) signals in harsh environments has gained immense attraction. The classical closed loop architectures like phase locked loop (PLL) have been used for many years for tracking purposes but for challenging applications their design procedure becomes intricate. This paper proposes and demonstrates the use of a quasi-open loop architecture to estimate the time varying carrier frequency of the GNSS signal. It is shown via simulation results that use of such type of scheme provides an additional degree of freedom for designing the whole architecture. Specially, we have shown that we can ease the design of the loop filter in harsh environments if we have this additional degree of freedom.
{"title":"A novel quasi open loop frequency estimator for GNSS signal tracking","authors":"M. Tahir, L. Presti, M. Fantino","doi":"10.1109/PLANS.2012.6236836","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236836","url":null,"abstract":"The problem of designing the robust architectures for tracking the global navigation satellite system (GNSS) signals in harsh environments has gained immense attraction. The classical closed loop architectures like phase locked loop (PLL) have been used for many years for tracking purposes but for challenging applications their design procedure becomes intricate. This paper proposes and demonstrates the use of a quasi-open loop architecture to estimate the time varying carrier frequency of the GNSS signal. It is shown via simulation results that use of such type of scheme provides an additional degree of freedom for designing the whole architecture. Specially, we have shown that we can ease the design of the loop filter in harsh environments if we have this additional degree of freedom.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124978384","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 : 2012-04-23DOI: 10.1109/PLANS.2012.6236866
P. Elliot, E. Rosario, B. Rama Rao, R. Davis, N. M. Marcus
Body-worn microstrip patch antennas made from e-textile (Electro-Textile) materials were designed, built, and tested for the 1575 MHz (L1) and 1227 MHz (L2) Global Positioning System (GPS) frequencies. E-textiles, including fabrics and foam sheets, are flexible, lightweight, wearable, conformal, and the materials are low cost compared to conventional antenna materials. A variety of materials were tested to determine suitability for e-textile antennas. Gain was measured and found to be a few dB lower than for patch antennas built with conventional copper-clad materials, largely due to higher ohmic losses in the conductive fabrics. Gain was also measured on a human body “phantom” which is a plastic shell filled with fluids formulated to replicate the electromagnetic properties of the body. Use of wearable antennas removes the antenna from the receiver which reduces the size and weight of the receiver, and also allows larger antennas to be used. This effort also helps prepare for possible future applications of multiple wearable antennas such as interference mitigation, anti-spoof, indoor position detection, multiple-input-multiple-output (MIMO) communications, and body-worn antennas for health monitoring or covert communications.
{"title":"E-textile microstrip patch antennas for GPS","authors":"P. Elliot, E. Rosario, B. Rama Rao, R. Davis, N. M. Marcus","doi":"10.1109/PLANS.2012.6236866","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236866","url":null,"abstract":"Body-worn microstrip patch antennas made from e-textile (Electro-Textile) materials were designed, built, and tested for the 1575 MHz (L1) and 1227 MHz (L2) Global Positioning System (GPS) frequencies. E-textiles, including fabrics and foam sheets, are flexible, lightweight, wearable, conformal, and the materials are low cost compared to conventional antenna materials. A variety of materials were tested to determine suitability for e-textile antennas. Gain was measured and found to be a few dB lower than for patch antennas built with conventional copper-clad materials, largely due to higher ohmic losses in the conductive fabrics. Gain was also measured on a human body “phantom” which is a plastic shell filled with fluids formulated to replicate the electromagnetic properties of the body. Use of wearable antennas removes the antenna from the receiver which reduces the size and weight of the receiver, and also allows larger antennas to be used. This effort also helps prepare for possible future applications of multiple wearable antennas such as interference mitigation, anti-spoof, indoor position detection, multiple-input-multiple-output (MIMO) communications, and body-worn antennas for health monitoring or covert communications.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125591564","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 : 2012-04-23DOI: 10.1109/PLANS.2012.6236900
Bidong Lu, J. Zhong, Minjian Zhao, Liyan Li
A near-far effect canceller is proposed for GPS high sensitivity receiver. It is designed to suppress the interfering signals by subtracting out their local reproduction simultaneously. Its performance depends on the accuracy of the data estimates. Furthermore, amplitude estimate is the key to the reproduction under the assumption of perfect code/carrier frequency synchronization by tracking. Two optional amplitude estimate scheme are proposed. The focus of this paper is on the near-far resistance capability. Simulation result shows that the canceller combined with two amplitude estimate scheme can suppress the interfering signal by 20dB and 30dB at least respectively. Considering of limited application, the second scheme is regarded as a candidate despite of better suppression performance. Moreover, the near-far effect canceller adopting the first amplitude estimate scheme not only improve the acquisition performance of weak satellites obviously, but also raise the maximum power level difference allowed by receiver to 38dB and improve the ability of near-far resistance by 17dB.
{"title":"A near-far effect canceller for GPS high sensitivity receiver","authors":"Bidong Lu, J. Zhong, Minjian Zhao, Liyan Li","doi":"10.1109/PLANS.2012.6236900","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236900","url":null,"abstract":"A near-far effect canceller is proposed for GPS high sensitivity receiver. It is designed to suppress the interfering signals by subtracting out their local reproduction simultaneously. Its performance depends on the accuracy of the data estimates. Furthermore, amplitude estimate is the key to the reproduction under the assumption of perfect code/carrier frequency synchronization by tracking. Two optional amplitude estimate scheme are proposed. The focus of this paper is on the near-far resistance capability. Simulation result shows that the canceller combined with two amplitude estimate scheme can suppress the interfering signal by 20dB and 30dB at least respectively. Considering of limited application, the second scheme is regarded as a candidate despite of better suppression performance. Moreover, the near-far effect canceller adopting the first amplitude estimate scheme not only improve the acquisition performance of weak satellites obviously, but also raise the maximum power level difference allowed by receiver to 38dB and improve the ability of near-far resistance by 17dB.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128788622","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 : 2012-04-23DOI: 10.1109/PLANS.2012.6236950
K. Brink, A. Soloviev
Vision-aided Inertial Navigation Systems (vINS) are capable of providing accurate six degree of freedom (6DoF) state estimation for autonomous vehicles (AVs) in the absence of Global Positioning System (GPS) and other global references. Features observed by a camera can be combined with measurements from an inertial measurement unit (IMU) in a filter to estimate the desired vehicle states. To do so, the rigid body transformation between cameras and the IMU must be known with high precision. Extended Kalman filters (EKF) and Unscented Kalman filters (UKF) have been used to calibrate camera and IMU systems requiring only a simple calibration target and moderate IMU-camera motion. This paper focuses on indoor applications where it is assumed a user is able to easily manipulate the sensor package. We extend the UKF filter to calibrate an IMU paired with an arbitrary number of cameras, with or without overlapping fields of view.
{"title":"Filter-based calibration for an IMU and multi-camera system","authors":"K. Brink, A. Soloviev","doi":"10.1109/PLANS.2012.6236950","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236950","url":null,"abstract":"Vision-aided Inertial Navigation Systems (vINS) are capable of providing accurate six degree of freedom (6DoF) state estimation for autonomous vehicles (AVs) in the absence of Global Positioning System (GPS) and other global references. Features observed by a camera can be combined with measurements from an inertial measurement unit (IMU) in a filter to estimate the desired vehicle states. To do so, the rigid body transformation between cameras and the IMU must be known with high precision. Extended Kalman filters (EKF) and Unscented Kalman filters (UKF) have been used to calibrate camera and IMU systems requiring only a simple calibration target and moderate IMU-camera motion. This paper focuses on indoor applications where it is assumed a user is able to easily manipulate the sensor package. We extend the UKF filter to calibrate an IMU paired with an arbitrary number of cameras, with or without overlapping fields of view.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129502202","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 : 2012-04-23DOI: 10.1109/PLANS.2012.6236985
Riyad A. El-laithy, Jidong Huang, M. Yeh
The Microsoft X-Box Kinect Sensor is a revolutionary new depth camera that is used in the gaming industry to capture motions of people and players efficiently using the technology of an RGB camera and infrared camera to differentiate depth. In the Microsoft X-Box, Kinect was used to sense 3D perception of human's motions. It can also be used for robotic applications, precisely for indoor navigation through the process of reverse engineering. Certain software packages were made available and are open source from “LibFreenect” for Linux machines, Microsoft's Kinect SDK using the Kinect namespace on Visual Studio 2010 Express (C++, C# or Visual Basic), and Google's released “Robotic Operating System (ROS)”. In order to claim that this sensor is capable of taking on such a task, we must be able to investigate thoroughly all factors that contribute to this and at the same time we must be able to understand its limitations to be applied and integrated properly with certain types of robots for accomplishing our purpose of achieving successful indoor navigation using proper algorithms. In this paper, the results from testing the Kinect sensor on an autonomous ground vehicle was given.
微软X-Box Kinect传感器是一款革命性的新型深度相机，用于游戏行业，利用RGB相机和红外相机的技术来区分深度，有效地捕捉人和玩家的动作。在微软的X-Box中，Kinect被用来感知人类动作的3D感知。它也可以用于机器人应用，通过逆向工程的过程精确地用于室内导航。LibFreenect为Linux机器提供了一些开源软件包，微软的Kinect SDK使用Visual Studio 2010 Express上的Kinect命名空间(c++、c#或Visual Basic)，以及谷歌发布的“机器人操作系统(ROS)”。为了声称这种传感器能够承担这样的任务，我们必须能够彻底调查促成这一点的所有因素，同时我们必须能够理解它的局限性，以便与某些类型的机器人适当地应用和集成，以实现我们使用适当算法实现成功室内导航的目的。本文给出了Kinect传感器在自主地面车辆上的测试结果。
{"title":"Study on the use of Microsoft Kinect for robotics applications","authors":"Riyad A. El-laithy, Jidong Huang, M. Yeh","doi":"10.1109/PLANS.2012.6236985","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236985","url":null,"abstract":"The Microsoft X-Box Kinect Sensor is a revolutionary new depth camera that is used in the gaming industry to capture motions of people and players efficiently using the technology of an RGB camera and infrared camera to differentiate depth. In the Microsoft X-Box, Kinect was used to sense 3D perception of human's motions. It can also be used for robotic applications, precisely for indoor navigation through the process of reverse engineering. Certain software packages were made available and are open source from “LibFreenect” for Linux machines, Microsoft's Kinect SDK using the Kinect namespace on Visual Studio 2010 Express (C++, C# or Visual Basic), and Google's released “Robotic Operating System (ROS)”. In order to claim that this sensor is capable of taking on such a task, we must be able to investigate thoroughly all factors that contribute to this and at the same time we must be able to understand its limitations to be applied and integrated properly with certain types of robots for accomplishing our purpose of achieving successful indoor navigation using proper algorithms. In this paper, the results from testing the Kinect sensor on an autonomous ground vehicle was given.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129594715","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 : 2012-04-23DOI: 10.1109/PLANS.2012.6236980
C. Taylor
Significant improvements in the accuracy of navigation state estimation have been previously demonstrated through the fusion of inertial measurement unit (IMU) and visual sensor data in both GPS-denied and GPS-enabled scenarios. Despite this improved navigation state accuracy, several significant hurdles remain before widespread acceptance of vision-aided navigation can be achieved. One significant bottleneck is the lack of accurate information about the performance of the vision-aided navigation algorithms. Many vision-aided navigation algorithms are implemented using some form of the Kalman Filter, thereby returning a covariance estimate that should correspond with the accuracy of the current navigation estimate. Unfortunately, a well known problem with the Kalman Filter is that its covariance estimates are inconsistent, i.e. the Kalman Filter estimates of uncertainty are significantly smaller than the true uncertainty achieved by the Kalman Filter. Recently a set of papers has introduced the concept of “observability-constrained” Kalman filtering that helps solve the consistency problem. In this paper, we apply the observability-constrained Kalman Filter to a vision-aided navigation problem and analyze its results. Significantly more accurate state and uncertainty estimates are achieved using the observability-constrained Kalman Filter. Unfortunately, the it is still not consistent, so a comparison with a batch, bundle adjustment approach is also performed to verify the possibility of consistent uncertainty estimation.
{"title":"An analysis of observability-constrained Kalman Filtering for vision-aided navigation","authors":"C. Taylor","doi":"10.1109/PLANS.2012.6236980","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236980","url":null,"abstract":"Significant improvements in the accuracy of navigation state estimation have been previously demonstrated through the fusion of inertial measurement unit (IMU) and visual sensor data in both GPS-denied and GPS-enabled scenarios. Despite this improved navigation state accuracy, several significant hurdles remain before widespread acceptance of vision-aided navigation can be achieved. One significant bottleneck is the lack of accurate information about the performance of the vision-aided navigation algorithms. Many vision-aided navigation algorithms are implemented using some form of the Kalman Filter, thereby returning a covariance estimate that should correspond with the accuracy of the current navigation estimate. Unfortunately, a well known problem with the Kalman Filter is that its covariance estimates are inconsistent, i.e. the Kalman Filter estimates of uncertainty are significantly smaller than the true uncertainty achieved by the Kalman Filter. Recently a set of papers has introduced the concept of “observability-constrained” Kalman filtering that helps solve the consistency problem. In this paper, we apply the observability-constrained Kalman Filter to a vision-aided navigation problem and analyze its results. Significantly more accurate state and uncertainty estimates are achieved using the observability-constrained Kalman Filter. Unfortunately, the it is still not consistent, so a comparison with a batch, bundle adjustment approach is also performed to verify the possibility of consistent uncertainty estimation.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121353558","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 : 2012-04-23DOI: 10.1109/PLANS.2012.6236912
A. Draganov, L. Haas, M. Harlacher
The IMRE Kalman filter is designed to compute the measurement update, when nonlinearities are weak enough to be treated as a perturbation. This paper explores four different nonlinear effects that any nonlinear filter should be able to handle. Based on simple, intuitive cases we show that other comparable known filters (the second-order filter, the UKF, the IEKF, the Gauss-Hermite quadrature filter, or the cubature filter) handle only some, but not all four nonlinear effects. In contrast, the IMRE Kalman filter addresses all four kinds of nonlinear effects, which makes it more general.
{"title":"The IMRE Kalman filter — A new Kalman filter extension for nonlinear applications","authors":"A. Draganov, L. Haas, M. Harlacher","doi":"10.1109/PLANS.2012.6236912","DOIUrl":"https://doi.org/10.1109/PLANS.2012.6236912","url":null,"abstract":"The IMRE Kalman filter is designed to compute the measurement update, when nonlinearities are weak enough to be treated as a perturbation. This paper explores four different nonlinear effects that any nonlinear filter should be able to handle. Based on simple, intuitive cases we show that other comparable known filters (the second-order filter, the UKF, the IEKF, the Gauss-Hermite quadrature filter, or the cubature filter) handle only some, but not all four nonlinear effects. In contrast, the IMRE Kalman filter addresses all four kinds of nonlinear effects, which makes it more general.","PeriodicalId":282304,"journal":{"name":"Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115073435","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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