Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235238
M. Zofka, Lars Töttel, Maximilian Zipfl, Marc Heinrich, Tobias Fleck, P. Schulz, Johann Marius Zöllner
Validation and verification of autonomous vehicles is still an unsolved problem. Although virtual approaches promise a cost efficient and reproducible solution, a most comprehensive and realistic representation of the real world traffic domain is required in order to make valuable statements about the performance of a highly automated driving (HAD) function. Models from different domain experts offer a repository of such representations. However, these models must be linked together for an extensive and uniform mapping of real world traffic domain for HAD performance assessment.Hereby, we propose the concept of a co-simulation architecture built upon the Robot Operating System (ROS) for both coupling and for integration of different domain expert models, immersion and stimulation of real pedestrians as well as AD systems into a common test system. This enables a unified way of generating ground truth for the performance assessment of multi-sensorial AD systems. We demonstrate the applicability of the ROS powered co-simulation by coupling behavior models in our mixed reality environment.
{"title":"Pushing ROS towards the Dark Side: A ROS-based Co-Simulation Architecture for Mixed-Reality Test Systems for Autonomous Vehicles","authors":"M. Zofka, Lars Töttel, Maximilian Zipfl, Marc Heinrich, Tobias Fleck, P. Schulz, Johann Marius Zöllner","doi":"10.1109/MFI49285.2020.9235238","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235238","url":null,"abstract":"Validation and verification of autonomous vehicles is still an unsolved problem. Although virtual approaches promise a cost efficient and reproducible solution, a most comprehensive and realistic representation of the real world traffic domain is required in order to make valuable statements about the performance of a highly automated driving (HAD) function. Models from different domain experts offer a repository of such representations. However, these models must be linked together for an extensive and uniform mapping of real world traffic domain for HAD performance assessment.Hereby, we propose the concept of a co-simulation architecture built upon the Robot Operating System (ROS) for both coupling and for integration of different domain expert models, immersion and stimulation of real pedestrians as well as AD systems into a common test system. This enables a unified way of generating ground truth for the performance assessment of multi-sensorial AD systems. We demonstrate the applicability of the ROS powered co-simulation by coupling behavior models in our mixed reality environment.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128340412","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235237
D. Sacharny, T. Henderson, Michael Cline
The advent of large-scale Unmanned Aircraft Systems (UAS) exploitation for urban tasks, such as delivery, has led to a great deal of research and development in the UAS Traffic Management (UTM) domain. The general approach at this time is to define a grid network for the area of operation, and then have UAS Service Suppliers (USS) pairwise deconflict any overlapping grid elements for their flights. Moreover, this analysis is performed on arbitrary flight paths through the airspace, and thus may impose a substantial computational burden in order to ensure strategic deconfliction (that is, no two flights are ever closer than the minimum required separation). However, the biggest drawback to this approach is the impact of contingencies on UTM operations. For example, if one UAS slows down, or goes off course, then strategic deconfliction is no longer guaranteed, and this can have a disastrous snowballing effect on a large number of flights. We propose a lane-based approach which not only allows a one-dimensional strategic deconfliction method, but provides structural support for alternative contingency handling methods with minimal impact on the overall UTM system. Methods for lane creation, path assignment through lanes, flight strategic deconfliction, and contingency handling are provided here.
{"title":"Large-Scale UAS Traffic Management (UTM) Structure","authors":"D. Sacharny, T. Henderson, Michael Cline","doi":"10.1109/MFI49285.2020.9235237","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235237","url":null,"abstract":"The advent of large-scale Unmanned Aircraft Systems (UAS) exploitation for urban tasks, such as delivery, has led to a great deal of research and development in the UAS Traffic Management (UTM) domain. The general approach at this time is to define a grid network for the area of operation, and then have UAS Service Suppliers (USS) pairwise deconflict any overlapping grid elements for their flights. Moreover, this analysis is performed on arbitrary flight paths through the airspace, and thus may impose a substantial computational burden in order to ensure strategic deconfliction (that is, no two flights are ever closer than the minimum required separation). However, the biggest drawback to this approach is the impact of contingencies on UTM operations. For example, if one UAS slows down, or goes off course, then strategic deconfliction is no longer guaranteed, and this can have a disastrous snowballing effect on a large number of flights. We propose a lane-based approach which not only allows a one-dimensional strategic deconfliction method, but provides structural support for alternative contingency handling methods with minimal impact on the overall UTM system. Methods for lane creation, path assignment through lanes, flight strategic deconfliction, and contingency handling are provided here.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126396385","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235265
F. Govaers, Hosam Alqaderi
In many data fusion applications, the parameter of interest only takes positive values. For example, it might be the goal to estimate a distance or to count instances of certain items. Optimal data fusion then should model the system state as a positive random variable, which has a probability density function that is restricted to the positive real axis. However, classical approaches based on normal densities fail here, in particular whenever the variance of the likelihood is rather large compared to the mean. In this paper, it is considered to model such random parameters with a Gamma distribution, since its support is positive and it is the maximum entropy distribution for such variables. For a Bayesian recursion, an approximative moment matching approach is proposed. An example within the framework of an autonomous simulation and further numerical considerations demonstrate the feasibility of the approach.
{"title":"A Gamma Filter for Positive Parameter Estimation","authors":"F. Govaers, Hosam Alqaderi","doi":"10.1109/MFI49285.2020.9235265","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235265","url":null,"abstract":"In many data fusion applications, the parameter of interest only takes positive values. For example, it might be the goal to estimate a distance or to count instances of certain items. Optimal data fusion then should model the system state as a positive random variable, which has a probability density function that is restricted to the positive real axis. However, classical approaches based on normal densities fail here, in particular whenever the variance of the likelihood is rather large compared to the mean. In this paper, it is considered to model such random parameters with a Gamma distribution, since its support is positive and it is the maximum entropy distribution for such variables. For a Bayesian recursion, an approximative moment matching approach is proposed. An example within the framework of an autonomous simulation and further numerical considerations demonstrate the feasibility of the approach.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130142866","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235218
Taeyeong Choi, Theodore P. Pavlic
Learning in robotic systems is largely constrained by the quality of the training data available to a robot learner. Robots may have to make multiple, repeated expensive excursions to gather this data or have humans in the loop to perform demonstrations to ensure reliable performance. The cost can be much higher when a robot embedded within a multi-robot system must learn from the complex aggregate of the many robots that surround it and may react to the learner’s motions. In our previous work [1], [2], we considered the problem of Remote Teammate Localization (ReTLo), where a single robot in a team uses passive observations of a nearby neighbor to accurately infer the position of robots outside of its sensory range even when robot-to-robot communication is not allowed in the system. We demonstrated a communication-free approach to show that the rearmost robot can use motion information of a single robot within its sensory range to predict the positions of all robots in the convoy. Here, we expand on that work with Selective Random Sampling (SRS), a framework that improves the ReTLo learning process by enabling the learner to actively deviate from its trajectory in ways that are likely to lead to better training samples and consequently gain accurate localization ability with fewer observations. By adding diversity to the learner’s motion, SRS simultaneously improves the learner’s predictions of all other teammates and thus can achieve similar performance as prior methods with less data.
{"title":"Automatic Discovery of Motion Patterns that Improve Learning Rate in Communication-Limited Multi-Robot Systems","authors":"Taeyeong Choi, Theodore P. Pavlic","doi":"10.1109/MFI49285.2020.9235218","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235218","url":null,"abstract":"Learning in robotic systems is largely constrained by the quality of the training data available to a robot learner. Robots may have to make multiple, repeated expensive excursions to gather this data or have humans in the loop to perform demonstrations to ensure reliable performance. The cost can be much higher when a robot embedded within a multi-robot system must learn from the complex aggregate of the many robots that surround it and may react to the learner’s motions. In our previous work [1], [2], we considered the problem of Remote Teammate Localization (ReTLo), where a single robot in a team uses passive observations of a nearby neighbor to accurately infer the position of robots outside of its sensory range even when robot-to-robot communication is not allowed in the system. We demonstrated a communication-free approach to show that the rearmost robot can use motion information of a single robot within its sensory range to predict the positions of all robots in the convoy. Here, we expand on that work with Selective Random Sampling (SRS), a framework that improves the ReTLo learning process by enabling the learner to actively deviate from its trajectory in ways that are likely to lead to better training samples and consequently gain accurate localization ability with fewer observations. By adding diversity to the learner’s motion, SRS simultaneously improves the learner’s predictions of all other teammates and thus can achieve similar performance as prior methods with less data.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125935358","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235248
Haohao Hu, Johannes Beck, M. Lauer, C. Stiller
In this work, we present an uniform B-spline based continuous fusion approach, which fuses the motion data from an inertial measurement unit and the pose data from a visual localization system accurately, efficiently and continu-ously. Currently, in the domain of robotics and autonomous driving, most of the ego motion fusion approaches are filter based or pose graph based. By using the filter based approaches like the Kalman Filter or the Particle Filter, usually, many parameters should be set carefully, which is a big overhead. Besides that, the filter based approaches can only fuse data in a time forwards direction, which is a big disadvantage in processing async data. Since the pose graph based approaches only fuse the pose data, the inertial measurement unit data should be integrated to estimate the corresponding pose data firstly, which can however bring accumulated error into the fusion system. Additionally, the filter based approaches and the pose graph based approaches only provide discrete fusion results, which may decrease the accuracy of the data processing steps afterwards. Since the fusion approach is generally needed for robots and automated driving vehicles, it is a major goal to make it more accurate, robust, efficient and continuous. Therefore, in this work, we address this problem and apply the axis-angle rotation representation method, the Rodrigues’ formula and the uniform B-spline implementation to solve the ego motion fusion problem continuously. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results.
{"title":"Continuous Fusion of IMU and Pose Data using Uniform B-Spline","authors":"Haohao Hu, Johannes Beck, M. Lauer, C. Stiller","doi":"10.1109/MFI49285.2020.9235248","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235248","url":null,"abstract":"In this work, we present an uniform B-spline based continuous fusion approach, which fuses the motion data from an inertial measurement unit and the pose data from a visual localization system accurately, efficiently and continu-ously. Currently, in the domain of robotics and autonomous driving, most of the ego motion fusion approaches are filter based or pose graph based. By using the filter based approaches like the Kalman Filter or the Particle Filter, usually, many parameters should be set carefully, which is a big overhead. Besides that, the filter based approaches can only fuse data in a time forwards direction, which is a big disadvantage in processing async data. Since the pose graph based approaches only fuse the pose data, the inertial measurement unit data should be integrated to estimate the corresponding pose data firstly, which can however bring accumulated error into the fusion system. Additionally, the filter based approaches and the pose graph based approaches only provide discrete fusion results, which may decrease the accuracy of the data processing steps afterwards. Since the fusion approach is generally needed for robots and automated driving vehicles, it is a major goal to make it more accurate, robust, efficient and continuous. Therefore, in this work, we address this problem and apply the axis-angle rotation representation method, the Rodrigues’ formula and the uniform B-spline implementation to solve the ego motion fusion problem continuously. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129840726","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235262
Aaron Grapentin, Dustin Lehmann, Ardjola Zhupa, T. Seel
Hand motion tracking is a key technology in several applications including ergonomic workplace assessment, human-machine interaction and neurological rehabilitation. Recent technological solutions are based on inertial measurement units (IMUs). They are less obtrusive than exoskeleton-based solutions and overcome the line-of-sight restrictions of optical systems. The number of sensors is crucial for usability, unobtrusiveness, and hardware cost. In this paper, we present a real-time capable, sparse motion tracking solution for hand motion tracking that requires only five IMUs, one on each of the distal finger segments and one on the back of the hand, in contrast to recently proposed full-setup solution with 16 IMUs. The method only uses gyroscope and accelerometer readings and avoids magnetometer readings, which enables unrestricted use in indoor environments, near ferromagnetic materials and electronic devices. We use a moving horizon estimation (MHE) approach that exploits kinematic constraints to track motions and performs long-term stable heading estimation. The proposed method is validated experimentally using a recently developed sensor system. It is found that the proposed method yields qualitatively good agreement of the estimated and the actual hand motion and that the estimates are long-term stable. The root-mean-square deviation between the fingertip position estimates of the sparse and the full setup are found to be in the range of 1 cm. The method is hence highly suitable for unobtrusive and non-restrictive motion tracking in a range of applications.
{"title":"Sparse Magnetometer-Free Real-Time Inertial Hand Motion Tracking","authors":"Aaron Grapentin, Dustin Lehmann, Ardjola Zhupa, T. Seel","doi":"10.1109/MFI49285.2020.9235262","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235262","url":null,"abstract":"Hand motion tracking is a key technology in several applications including ergonomic workplace assessment, human-machine interaction and neurological rehabilitation. Recent technological solutions are based on inertial measurement units (IMUs). They are less obtrusive than exoskeleton-based solutions and overcome the line-of-sight restrictions of optical systems. The number of sensors is crucial for usability, unobtrusiveness, and hardware cost. In this paper, we present a real-time capable, sparse motion tracking solution for hand motion tracking that requires only five IMUs, one on each of the distal finger segments and one on the back of the hand, in contrast to recently proposed full-setup solution with 16 IMUs. The method only uses gyroscope and accelerometer readings and avoids magnetometer readings, which enables unrestricted use in indoor environments, near ferromagnetic materials and electronic devices. We use a moving horizon estimation (MHE) approach that exploits kinematic constraints to track motions and performs long-term stable heading estimation. The proposed method is validated experimentally using a recently developed sensor system. It is found that the proposed method yields qualitatively good agreement of the estimated and the actual hand motion and that the estimates are long-term stable. The root-mean-square deviation between the fingertip position estimates of the sparse and the full setup are found to be in the range of 1 cm. The method is hence highly suitable for unobtrusive and non-restrictive motion tracking in a range of applications.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132705754","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235260
Kailai Li, F. Pfaff, U. Hanebeck
We present a novel deterministic sampling approach for von Mises–Fisher distributions of arbitrary dimensions. Following the idea of the unscented transform, samples of configurable size are drawn isotropically on the hypersphere while preserving the mean resultant vector of the underlying distribution. Based on these samples, a von Mises–Fisher filter is proposed for nonlinear estimation of hyperspherical states. Compared with existing von Mises–Fisher-based filtering schemes, the proposed filter exhibits superior hyperspherical tracking performance.
{"title":"Nonlinear von Mises–Fisher Filtering Based on Isotropic Deterministic Sampling","authors":"Kailai Li, F. Pfaff, U. Hanebeck","doi":"10.1109/MFI49285.2020.9235260","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235260","url":null,"abstract":"We present a novel deterministic sampling approach for von Mises–Fisher distributions of arbitrary dimensions. Following the idea of the unscented transform, samples of configurable size are drawn isotropically on the hypersphere while preserving the mean resultant vector of the underlying distribution. Based on these samples, a von Mises–Fisher filter is proposed for nonlinear estimation of hyperspherical states. Compared with existing von Mises–Fisher-based filtering schemes, the proposed filter exhibits superior hyperspherical tracking performance.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"16 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133487595","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235268
Jason L. Williams, Shu Jiang, M. O'Brien, Glenn Wagner, E. Hernández, Mark Cox, Alex Pitt, R. Arkin, N. Hudson
While robots have long been proposed as a tool to reduce human personnel’s exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.
{"title":"Online 3D Frontier-Based UGV and UAV Exploration Using Direct Point Cloud Visibility","authors":"Jason L. Williams, Shu Jiang, M. O'Brien, Glenn Wagner, E. Hernández, Mark Cox, Alex Pitt, R. Arkin, N. Hudson","doi":"10.1109/MFI49285.2020.9235268","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235268","url":null,"abstract":"While robots have long been proposed as a tool to reduce human personnel’s exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"492 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116030226","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235240
Juncong Fei, Wenbo Chen, Philipp Heidenreich, Sascha Wirges, C. Stiller
3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. LiDAR and camera are two commonly used sensor modalities for this task, which should provide complementary information. Unexpectedly, LiDAR-only detection methods tend to outperform multisensor fusion methods in public benchmarks. Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information. In this paper, we propose a generalization of PointPainting to be able to apply fusion at different levels. After the semantic augmentation of the point cloud, we encode raw point data in pillars to get geometric features and semantic point data in voxels to get semantic features and fuse them in an effective way. Experimental results on the KITTI test set show that SemanticVoxels achieves state-of-the-art performance in both 3D and bird’s eye view pedestrian detection benchmarks. In particular, our approach demonstrates its strength in detecting challenging pedestrian cases and outperforms current state-of-the-art approaches.
{"title":"SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation","authors":"Juncong Fei, Wenbo Chen, Philipp Heidenreich, Sascha Wirges, C. Stiller","doi":"10.1109/MFI49285.2020.9235240","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235240","url":null,"abstract":"3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. LiDAR and camera are two commonly used sensor modalities for this task, which should provide complementary information. Unexpectedly, LiDAR-only detection methods tend to outperform multisensor fusion methods in public benchmarks. Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information. In this paper, we propose a generalization of PointPainting to be able to apply fusion at different levels. After the semantic augmentation of the point cloud, we encode raw point data in pillars to get geometric features and semantic point data in voxels to get semantic features and fuse them in an effective way. Experimental results on the KITTI test set show that SemanticVoxels achieves state-of-the-art performance in both 3D and bird’s eye view pedestrian detection benchmarks. In particular, our approach demonstrates its strength in detecting challenging pedestrian cases and outperforms current state-of-the-art approaches.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132944957","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 : 2020-09-14DOI: 10.1109/MFI49285.2020.9235257
Rodolphe Dubois, A. Eudes, V. Fremont
This paper introduces a new dataset dedicated to multi-robot stereo-visual and inertial Simultaneous Localization And Mapping (SLAM). This dataset consists in five indoor multi-robot scenarios acquired with ground and aerial robots in a former Air Museum at ONERA Meudon, France. Those scenarios were designed to exhibit some specific opportunities and challenges associated to collaborative SLAM. Each scenario includes synchronized sequences between multiple robots with stereo images and inertial measurements. They also exhibit explicit direct interactions between robots through the detection of mounted AprilTag markers [1]. Ground-truth trajectories for each robot were computed using Structure-from-Motion algorithms and constrained with the detection of fixed AprilTag markers placed as beacons on the experimental area. Those scenarios have been benchmarked on state-of-the-art monocular, stereo and visual-inertial SLAM algorithms to provide a baseline of the single-robot performances to be enhanced in collaborative frameworks.
{"title":"AirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial Simultaneous Localization And Mapping","authors":"Rodolphe Dubois, A. Eudes, V. Fremont","doi":"10.1109/MFI49285.2020.9235257","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235257","url":null,"abstract":"This paper introduces a new dataset dedicated to multi-robot stereo-visual and inertial Simultaneous Localization And Mapping (SLAM). This dataset consists in five indoor multi-robot scenarios acquired with ground and aerial robots in a former Air Museum at ONERA Meudon, France. Those scenarios were designed to exhibit some specific opportunities and challenges associated to collaborative SLAM. Each scenario includes synchronized sequences between multiple robots with stereo images and inertial measurements. They also exhibit explicit direct interactions between robots through the detection of mounted AprilTag markers [1]. Ground-truth trajectories for each robot were computed using Structure-from-Motion algorithms and constrained with the detection of fixed AprilTag markers placed as beacons on the experimental area. Those scenarios have been benchmarked on state-of-the-art monocular, stereo and visual-inertial SLAM algorithms to provide a baseline of the single-robot performances to be enhanced in collaborative frameworks.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125201428","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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