DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry

TL;DR

DAMS-LIO is a lightweight, modular LiDAR-inertial odometry system that adaptively fuses sensor data based on degeneration detection, achieving high accuracy in complex environments.

Contribution

The paper introduces a degeneration-aware, modular sensor-fusion pipeline for LiDAR-inertial odometry that improves accuracy and robustness in challenging environments.

Findings

Higher accuracy demonstrated via CRLB theory and simulations.

Achieves real-time performance in complex environments.

Outperforms state-of-the-art methods in challenging datasets.

Abstract

With robots being deployed in increasingly complex environments like underground mines and planetary surfaces, the multi-sensor fusion method has gained more and more attention which is a promising solution to state estimation in the such scene. The fusion scheme is a central component of these methods. In this paper, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is detected. Both the Cramer-Rao Lower Bound (CRLB) theory and simulation test are used to demonstrate the higher accuracy of our method compared to methods using a single observation. Furthermore, the proposed system is evaluated in perceptually challenging datasets against various state-of-the-art sensor-fusion methods. The results show that the proposed system achieves real-time and high estimation accuracy performance despite the challenging environment and poor observations.