Fabian Arzberger , Tim Schubert , Fabian Wiecha , Jasper Zevering , Julian Rothe , Dorit Borrmann , Sergio Montenegro , Andreas Nüchter
{"title":"用于球形移动测绘系统多传感器姿态估计的德尔塔滤波器和卡尔曼滤波器设计","authors":"Fabian Arzberger , Tim Schubert , Fabian Wiecha , Jasper Zevering , Julian Rothe , Dorit Borrmann , Sergio Montenegro , Andreas Nüchter","doi":"10.1016/j.robot.2024.104852","DOIUrl":null,"url":null,"abstract":"<div><div>Spherical mobile mapping systems are not thoroughly studied in terms of inertial pose estimation filtering. The underlying inherent rolling motion introduces high angular velocities and aggressive system dynamics around all principal axes. This motion profile also needs different modeling compared to state-of-the-art competitors, which heavily focus on more rotationally-restricted systems such as UAV, handheld, or cars. In this work we compare our previously proposed “Delta-filter”, which was heavily motivated by the sensors inability to provide covariance estimations, with a Kalman-filter design using a covariance model. Both filters fuse two 6-DoF pose estimators with a motion model in real-time, however the designs are theoretically suitable for an arbitrary number of estimators. We evaluate the trajectories against ground truth pose measurement from an OptiTrack™ motion capturing system. Furthermore, as our spherical systems are equipped with laser-scanners, we evaluate the resulting point clouds against ground truth maps available from a Riegl VZ400 terrestrial laser-scanner (TLS). Our source code and datasets can be found on github (Arzberger, 2023).</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"184 ","pages":"Article 104852"},"PeriodicalIF":4.3000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Delta- and Kalman-filter designs for multi-sensor pose estimation on spherical mobile mapping systems\",\"authors\":\"Fabian Arzberger , Tim Schubert , Fabian Wiecha , Jasper Zevering , Julian Rothe , Dorit Borrmann , Sergio Montenegro , Andreas Nüchter\",\"doi\":\"10.1016/j.robot.2024.104852\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Spherical mobile mapping systems are not thoroughly studied in terms of inertial pose estimation filtering. The underlying inherent rolling motion introduces high angular velocities and aggressive system dynamics around all principal axes. This motion profile also needs different modeling compared to state-of-the-art competitors, which heavily focus on more rotationally-restricted systems such as UAV, handheld, or cars. In this work we compare our previously proposed “Delta-filter”, which was heavily motivated by the sensors inability to provide covariance estimations, with a Kalman-filter design using a covariance model. Both filters fuse two 6-DoF pose estimators with a motion model in real-time, however the designs are theoretically suitable for an arbitrary number of estimators. We evaluate the trajectories against ground truth pose measurement from an OptiTrack™ motion capturing system. Furthermore, as our spherical systems are equipped with laser-scanners, we evaluate the resulting point clouds against ground truth maps available from a Riegl VZ400 terrestrial laser-scanner (TLS). Our source code and datasets can be found on github (Arzberger, 2023).</div></div>\",\"PeriodicalId\":49592,\"journal\":{\"name\":\"Robotics and Autonomous Systems\",\"volume\":\"184 \",\"pages\":\"Article 104852\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Robotics and Autonomous Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0921889024002367\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Autonomous Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0921889024002367","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Delta- and Kalman-filter designs for multi-sensor pose estimation on spherical mobile mapping systems
Spherical mobile mapping systems are not thoroughly studied in terms of inertial pose estimation filtering. The underlying inherent rolling motion introduces high angular velocities and aggressive system dynamics around all principal axes. This motion profile also needs different modeling compared to state-of-the-art competitors, which heavily focus on more rotationally-restricted systems such as UAV, handheld, or cars. In this work we compare our previously proposed “Delta-filter”, which was heavily motivated by the sensors inability to provide covariance estimations, with a Kalman-filter design using a covariance model. Both filters fuse two 6-DoF pose estimators with a motion model in real-time, however the designs are theoretically suitable for an arbitrary number of estimators. We evaluate the trajectories against ground truth pose measurement from an OptiTrack™ motion capturing system. Furthermore, as our spherical systems are equipped with laser-scanners, we evaluate the resulting point clouds against ground truth maps available from a Riegl VZ400 terrestrial laser-scanner (TLS). Our source code and datasets can be found on github (Arzberger, 2023).
期刊介绍:
Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems.
Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.