The paper discusses LIO-EKF, a system for LiDAR-inertial odometry based on point-to-point registration and extended Kalman filters. This system reduces drift in autonomous robot navigation by combining LiDAR scans and IMU measurements. LIO-EKF provides robust and effective ego-motion estimation, performing on par with state-of-the-art LiDAR-inertial odometry systems but with faster computation. The system also features an adaptive data association, reducing the need for parameter tuning. The system was tested on various platforms and environments, demonstrating its versatility and effectiveness.
Publication date: 20 Nov 2023
Project Page: Not provided
Paper: https://arxiv.org/pdf/2311.09887
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