M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance
We present a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data. To initiate the method, we use the prior GNSS pose information. We then perform motion estimation in real-time, which produces robust motion estimates in a global frame b...
| Príomhchruthaitheoirí: | , , , |
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| Formáid: | Conference item |
| Teanga: | English |
| Foilsithe / Cruthaithe: |
IEEE
2023
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| _version_ | 1826310856318648320 |
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| author | Das, S Mahabadi, N Fallon, M Chatterjee, S |
| author_facet | Das, S Mahabadi, N Fallon, M Chatterjee, S |
| author_sort | Das, S |
| collection | OXFORD |
| description | We present a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data. To initiate the method, we use the prior GNSS pose information. We then perform motion estimation in real-time, which produces robust motion estimates in a global frame by fusing lidar and IMU signals with GNSS translation components using a factor graph framework. We also propose methods to account for signal loss with a novel synchronization and fusion mechanism. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (5 sequences for a total of ≈ 7 Km). From our evaluations, we show an average improvement of 61% in relative translation and 42% rotational error compared to a state-of-the-art estimator fusing a single lidar/inertial sensor pair, in sensor dropout scenarios. |
| first_indexed | 2024-03-07T07:58:11Z |
| format | Conference item |
| id | oxford-uuid:e39d26ff-5957-4f8f-ad5a-441bd08e3583 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:58:11Z |
| publishDate | 2023 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:e39d26ff-5957-4f8f-ad5a-441bd08e35832023-09-01T12:50:26ZM-LIO: Multi-lidar, multi-IMU odometry with sensor dropout toleranceConference itemhttp://purl.org/coar/resource_type/c_5794uuid:e39d26ff-5957-4f8f-ad5a-441bd08e3583EnglishSymplectic ElementsIEEE2023Das, SMahabadi, NFallon, MChatterjee, SWe present a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data. To initiate the method, we use the prior GNSS pose information. We then perform motion estimation in real-time, which produces robust motion estimates in a global frame by fusing lidar and IMU signals with GNSS translation components using a factor graph framework. We also propose methods to account for signal loss with a novel synchronization and fusion mechanism. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (5 sequences for a total of ≈ 7 Km). From our evaluations, we show an average improvement of 61% in relative translation and 42% rotational error compared to a state-of-the-art estimator fusing a single lidar/inertial sensor pair, in sensor dropout scenarios. |
| spellingShingle | Das, S Mahabadi, N Fallon, M Chatterjee, S M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance |
| title | M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance |
| title_full | M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance |
| title_fullStr | M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance |
| title_full_unstemmed | M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance |
| title_short | M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance |
| title_sort | m lio multi lidar multi imu odometry with sensor dropout tolerance |
| work_keys_str_mv | AT dass mliomultilidarmultiimuodometrywithsensordropouttolerance AT mahabadin mliomultilidarmultiimuodometrywithsensordropouttolerance AT fallonm mliomultilidarmultiimuodometrywithsensordropouttolerance AT chatterjees mliomultilidarmultiimuodometrywithsensordropouttolerance |