Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

In this letter, we present tightly coupled LiDAR-IMU-leg odometry, which is robust to challenging conditions such as featureless environments and deformable terrains. We developed an online learning-based leg kinematics model named the neural leg kinematics model, which incorporates tactile information (foot reaction force) to implicitly express the nonlinear dynamics between robot feet and the ground. Online training of this model enhances its adaptability to weight load changes of a robot (e.g., assuming delivery or transportation tasks) and terrain conditions. According to the \textit{neural adaptive leg odometry factor} and online uncertainty estimation of the leg kinematics model-based motion predictions, we jointly solve online training of this kinematics model and odometry estimation on a unified factor graph to retain the consistency of both. The proposed method was verified through real experiments using a quadruped robot in two challenging situations: 1) a sandy beach, representing an extremely featureless area with a deformable terrain, and 2) a campus, including multiple featureless areas and terrain types of asphalt, gravel (deformable terrain), and grass. Experimental results showed that our odometry estimation incorporating the \textit{neural leg kinematics model} outperforms state-of-the-art works. Our project page is available for further details: https://takuokawara.github.io/RAL2025_project_page/

Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling

In this work, to facilitate the real-time processing of multi-scan registration error minimization on factor graphs, we devise a point cloud downsampling algorithm based on coreset extraction. This algorithm extracts a subset of the residuals of input points such that the subset yields exactly the same quadratic error function as that of the original set for a given pose. This enables a significant reduction in the number of residuals to be evaluated without approximation errors at the sampling point. Using this algorithm, we devise a complete SLAM framework that consists of odometry estimation based on sliding window optimization and global trajectory optimization based on registration error minimization over the entire map, both of which can run in real time on a standard CPU. The experimental results demonstrate that the proposed framework outperforms state-of-the-art CPU-based SLAM frameworks without the use of GPU acceleration.

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.