Robust Odometry and Mapping for Multi-LiDAR Systems With Online Extrinsic Calibration

Jianhao Jiao

Abstract:

Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient
measurements, which is promising for simultaneous localization
and mapping (SLAM). This article proposes a system to achieve
robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs. Our approach starts with measurement
preprocessing to extract edge and planar features from raw measurements. After a motion and extrinsic initialization procedure,
a sliding window-based multi-LiDAR odometry runs onboard to
estimate poses with an online calibration refinement and convergence identification. We further develop a mapping algorithm to
construct a global map and optimize poses with sufficient features
together with a method to capture and reduce data uncertainty. We
validate our approach’s performance with extensive experiments
on 10 sequences (4.60-km total length) for the calibration and
SLAM and compare it against the state of the art. We demonstrate
that the proposed work is a complete, robust, and extensible system
for various multi-LiDAR setups. The source code, datasets, and
demonstrations are available at: https://ram-lab.com/file/site/mloam.