EMV-LIO: An Efficient Multiple Vision aided LiDAR-Inertial Odometry

TL;DR

EMV-LIO is a multi-sensor fusion system that combines multiple cameras with LiDAR and inertial sensors to enhance accuracy, robustness, and efficiency in odometry, outperforming existing methods like LVI-SAM.

Contribution

The paper introduces EMV-LIO, a novel multi-vision aided LiDAR-inertial odometry system that improves robustness and efficiency through advanced sensor fusion and data processing techniques.

Findings

Improved accuracy over LVI-SAM.

Enhanced robustness in challenging environments.

Increased computational efficiency.

Abstract

To deal with the degeneration caused by the incomplete constraints of single sensor, multi-sensor fusion strategies especially in LiDAR-vision-inertial fusion area have attracted much interest from both the industry and the research community in recent years. Considering that a monocular camera is vulnerable to the influence of ambient light from a certain direction and fails, which makes the system degrade into a LiDAR-inertial system, multiple cameras are introduced to expand the visual observation so as to improve the accuracy and robustness of the system. Besides, removing LiDAR's noise via range image, setting condition for nearest neighbor search, and replacing kd-Tree with ikd-Tree are also introduced to enhance the efficiency. Based on the above, we propose an Efficient Multiple vision aided LiDAR-inertial odometry system (EMV-LIO), and evaluate its performance on both open datasets and our custom datasets. Experiments show that the algorithm is helpful to improve the accuracy, robustness and efficiency of the whole system compared with LVI-SAM. Our implementation will be available upon acceptance.