51 citations · 117 across the 24 of their papers we have counts for
5 papers · 1 filter
GLIM: 3D Range-Inertial Localization and Mapping with GPU-Accelerated Scan Matching Factors
Kenji Koide, Masashi Yokozuka, Shuji Oishi +1
This article presents GLIM, a 3D range-inertial localization and mapping framework with GPU-accelerated scan matching factors. The odometry estimation module of GLIM employs a comb…
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…
MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents a 6-DoF range-based Monte Carlo localization method with a GPU-accelerated Stein particle filter. To update a massive amount of particles, we propose a Gauss-Ne…
Tightly-Coupled LiDAR-IMU-Wheel Odometry with Online Calibration of a Kinematic Model for Skid-Steering Robots
Taku Okawara, Kenji Koide, Shuji Oishi +4
Tunnels and long corridors are challenging environments for mobile robots because a LiDAR point cloud should degenerate in these environments. To tackle point cloud degeneration, t…
Tightly Coupled Range Inertial Localization on a 3D Prior Map Based on Sliding Window Factor Graph Optimization
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents a range inertial localization algorithm for a 3D prior map. The proposed algorithm tightly couples scan-to-scan and scan-to-map point cloud registration factors…