Conference item
M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Reviewed (other)
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 4.5MB, Terms of use)
-
- Publisher copy:
- 10.1109/IV55152.2023.10186548
Authors
- Publisher:
- IEEE
- Publication date:
- 2023-07-27
- Acceptance date:
- 2023-01-06
- Event title:
- The 35th IEEE Intelligent Vehicles Symposium (IV 2023)
- Event series:
- IEEE Intelligent Vehicles Symposium
- Event location:
- Anchorage, Alaska, USA
- Event website:
- https://2023.ieee-iv.org/
- Event start date:
- 2023-06-04
- Event end date:
- 2023-06-07
- DOI:
- EISSN:
-
2642-7214
- ISSN:
-
1931-0587
- Language:
-
English
- Keywords:
- Pubs id:
-
1341455
- Local pid:
-
pubs:1341455
- Deposit date:
-
2023-05-18
- ARK identifier:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2023
- Rights statement:
- © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- Notes:
- This paper was presented at the IV 2023 Conference, 4th - 7th June 2023. This is the accepted manuscript version of the article. The final version is available from IEEE at: 10.1109/IV55152.2023.10186548
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