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

Abstract

The fusion scheme is crucial to the multi-sensor fusion method that is the promising solution to the state estimation in complex and extreme environments like underground mines and planetary surfaces. In this work, 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 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 realtime and high estimation accuracy performance despite the challenging environment and poor observations.

R-LIO: Rotating Lidar Inertial Odometry and Mapping

“…It is well known that LiDAR-based methods can capture the details of the environment from a long distance, so as to obtain the geometric information of the surroundings. However, this method often fails in degenerated environments such as a long tunnel or an open field, since features extracted by LiDAR are almost the same everywhere [3] [24]. Although vision-based methods are especially suitable for place recognition tasks and perform well in texture-rich environments, most of them are extremely sensitive to light changes, fast motion, and initialization.…”