Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments release_rllzigcqcvgezarpjyakroq7ya

by Shibo Zhao, Hengrui Zhang, Peng Wang, Lucas Nogueira, Sebastian Scherer

Released as a article .

2021  

Abstract

We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, visual-inertial odometry, and laser-inertial odometry. The visual-inertial odometry and laser-inertial odometry provide the pose prior to constrain the IMU bias and receive the motion prediction from IMU odometry. To ensure high performance in real-time, we apply a dynamic octree that only consumes 10 with a static KD-tree. The proposed system was deployed on drones and ground robots, as part of Team Explorer's effort to the DARPA Subterranean Challenge where the team won 1^st and 2^nd place in the Tunnel and Urban Circuits, respectively.
In text/plain format

Archived Files and Locations

application/pdf  5.7 MB
file_k2hzkcwgqnchbpzqtvwfypxnsa
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-04-30
Version   v1
Language   en ?
arXiv  2104.14938v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 05fccd7c-4266-4d96-9578-d277975d42af
API URL: JSON