Range-based 6-DoF Monte Carlo SLAM with Gradient-guided Particle Filter on GPU

This paper presents range-based 6-DoF Monte Carlo SLAM with a gradient-guided particle update strategy. While non-parametric state estimation methods, such as particle filters, are robust in situations with high ambiguity, they are known to be unsuitable for high-dimensional problems due to the curse of dimensionality. To address this issue, we propose a particle update strategy that improves the sampling efficiency by using the gradient information of the likelihood function to guide particles toward its mode. Additionally, we introduce a keyframe-based map representation that represents the global map as a set of past frames (i.e., keyframes) to mitigate memory consumption. The keyframe poses for each particle are corrected using a simple loop closure method to maintain trajectory consistency. The combination of gradient information and keyframe-based map representation significantly enhances sampling efficiency and reduces memory usage compared to traditional RBPF approaches. To process a large number of particles (e.g., 100,000 particles) in real-time, the proposed framework is designed to fully exploit GPU parallel processing. Experimental results demonstrate that the proposed method exhibits extreme robustness to state ambiguity and can even deal with kidnapping situations, such as when the sensor moves to different floors via an elevator, with minimal heuristics.

LiDAR-Inertial Odometry Based on Extended Kalman Filter

LiDAR-Inertial Odometry (LIO) is typically implemented using an optimization-based approach, with the factor graph often being employed due to its capability to seamlessly integrate residuals from both LiDAR and IMU measurements. Conversely, a recent study has demonstrated that accurate LIO can also be achieved using a loosely-coupled method. Inspired by this advancements, we present a novel LIO method that leverages the recursive Bayes filter, solved via the Extended Kalman Filter (EKF) - herein referred to as LIO-EKF. Within LIO-EKF, prior and likelihood distributions are computed using IMU preintegration and scan matching between LiDAR and local map point clouds, and the pose, velocity, and IMU biases are updated through the EKF process. Through experiments with the Newer College dataset, we demonstrate that LIO-EKF achieves precise trajectory tracking and mapping. Its accuracy is comparable to that of the state-of-the-art methods in both tightly- and loosely-coupled methods.