Selective Kalman Filter: When and How to Fuse Multi-Sensor Information to Overcome Degeneracy in SLAM

Research trends in SLAM systems are now focusing more on multi-sensor fusion to handle challenging and degenerative environments. However, most existing multi-sensor fusion SLAM methods mainly use all of the data from a range of sensors, a strategy we refer to as the all-in method. This method, while merging the benefits of different sensors, also brings in their weaknesses, lowering the robustness and accuracy and leading to high computational demands. To address this, we propose a new fusion approach -- Selective Kalman Filter -- to carefully choose and fuse information from multiple sensors (using LiDAR and visual observations as examples in this paper). For deciding when to fuse data, we implement degeneracy detection in LiDAR SLAM, incorporating visual measurements only when LiDAR SLAM exhibits degeneracy. Regarding degeneracy detection, we propose an elegant yet straightforward approach to determine the degeneracy of LiDAR SLAM and to identify the specific degenerative direction. This method fully considers the coupled relationship between rotational and translational constraints. In terms of how to fuse data, we use visual measurements only to update the specific degenerative states. As a result, our proposed method improves upon the all-in method by greatly enhancing real-time performance due to less processing visual data, and it introduces fewer errors from visual measurements. Experiments demonstrate that our method for degeneracy detection and fusion, in addressing degeneracy issues, exhibits higher precision and robustness compared to other state-of-the-art methods, and offers enhanced real-time performance relative to the all-in method. The code is openly available.

I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry

LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the problem: IEKF only iterates over the observation equation, relying on a rough estimate of the initial state, which is insufficient to fully eliminate motion distortion in the input point cloud; the system process noise is difficult to be determined during state estimation of the complex motions; and the varying motion models across different sensor carriers. To address these issues, we propose the Dual-Iteration Extended Kalman Filter (I2EKF) and the LiDAR odometry based on I2EKF (I2EKF-LO). This approach not only iterates over the observation equation but also leverages state updates to iteratively mitigate motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts process noise based on the confidence level of prior predictions during state estimation and establishes motion models for different sensor carriers to achieve accurate and efficient state estimation. Comprehensive experiments demonstrate that I2EKF-LO achieves outstanding levels of accuracy and computational efficiency in the realm of LiDAR odometry. Additionally, to foster community development, our code is open-sourced.https://github.com/YWL0720/I2EKF-LO.