From the 1 of 9 linked papers with an AI index.
9 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…
D-SLAMSpoof: An Environment-Agnostic LiDAR Spoofing Attack using Dynamic Point Cloud Injection
Rokuto Nagata, Kenji Koide, Kazuma Ikeda +2
In this work, we introduce Dynamic SLAMSpoof (D-SLAMSpoof), a novel attack that compromises LiDAR SLAM even in feature-rich environments. The attack leverages LiDAR spoofing, which…
MirrorDrift: Actuated Mirror-Based Attacks on LiDAR SLAM
Rokuto Nagata, Kenji Koide, Kazuma Ikeda +3
LiDAR SLAM provides high-accuracy localization but is fragile to point-cloud corruption because scan matching assumes geometric consistency. Prior physical attacks on LiDAR SLAM la…
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…