Conference item
BoxGraph: semantic place recognition and pose estimation from 3D LiDAR
- Abstract:
- This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its shape. Optimal vertex association across graphs allows for full 6-Degree-of-Freedom (DoF) pose estimation and place recognition by measuring similarity. This representation is very concise, condensing the size of maps by a factor of 25 against the state-of-the-art, requiring only 3 kB to represent a 1.4 MB laser scan. We verify the efficacy of our system on the SemanticKITTI dataset, where we achieve a new state-of-the-art in place recognition, with an average of 88.4 % recall at 100 % precision where the next closest competitor follows with 64.9 %. We also show accurate metric pose estimation performance - estimating 6-DoF pose with median errors of 10cm and 0.33 deg.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 2.4MB, Terms of use)
-
- Publisher copy:
- 10.1109/IROS47612.2022.9981266
Authors
- Publisher:
- IEEE
- Host title:
- 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Pages:
- 7004-7011
- Series:
- IROS 2022
- Series number:
- 35
- Publication date:
- 2021-12-26
- Event title:
- 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Event series:
- IEEE/RSJ International Conference on Intelligent Robots and Systems
- Event location:
- Kyoto, Japan
- Event website:
- https://iros2022.org/
- Event start date:
- 2022-10-23
- Event end date:
- 2022-10-27
- DOI:
- EISSN:
-
2153-0866
- ISSN:
-
2153-0858
- ISBN:
- 9781665479271
- Language:
-
English
- Keywords:
- Pubs id:
-
1325843
- Local pid:
-
pubs:1325843
- Deposit date:
-
2023-02-16
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2022
- Rights statement:
- ©2022 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 is the accepted manuscript version of the article. The final version is available from IEEE at: 10.1109/IROS47612.2022.9981266
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