From the 1 of 6 linked papers with an AI index.
6 papers
KING: Embodiment-Aware Kinematic Graph Neural Network for Unified Motion Representation of Legged and Wheeled Robots
Taku Okawara, Aoki Takanose, Kenji Koide +2
Kinematic models provide reliable motion constraints for odometry estimation in featureless environments, where exteroceptive sensing degrades and IMU integration drifts. Learning-…
Image-to-Point Cloud Registration Made Easy with Rectified Flow-based LiDAR Upsampling
Reon Tabata, Kenji Koide, Shuji Oishi +4
The paper presents a method that converts a sparse LiDAR scan into a dense intensity image using conditional rectified flow, matches it to a camera image, and estimates the 6‑DoF p…
Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
Taku Okawara, Kenji Koide, Aoki Takanose +4
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…
Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling
Kenji Koide, Aoki Takanose, Shuji Oishi +1
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…
Range-based 6-DoF Monte Carlo SLAM with Gradient-guided Particle Filter on GPU
Takumi Nakao, Kenji Koide, Aoki Takanose +3
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…
Tightly-Coupled LiDAR-IMU-Wheel Odometry with an Online Neural Kinematic Model Learning via Factor Graph Optimization
Taku Okawara, Kenji Koide, Shuji Oishi +4
Environments lacking geometric features (e.g., tunnels and long straight corridors) are challenging for LiDAR-based odometry algorithms because LiDAR point clouds degenerate in suc…