51 citations · 110 across the 17 of their papers we have counts for
17 papers
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…
3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm
Koki Aoki, Kenji Koide, Shuji Oishi +3
This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce m…
Exact Point Cloud Downsampling for Fast and Accurate Global Trajectory Optimization
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents a point cloud downsampling algorithm for fast and accurate trajectory optimization based on global registration error minimization. The proposed algorithm selec…
Single-Shot Global Localization via Graph-Theoretic Correspondence Matching
Shigemichi Matsuzaki, Kenji Koide, Shuji Oishi +2
This paper describes a method of global localization based on graph-theoretic association of instances between a query and the prior map. The proposed framework employs corresponde…
General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents an open source LiDAR-camera calibration toolbox that is general to LiDAR and camera projection models, requires only one pairing of LiDAR and camera data withou…
Generalized LOAM: LiDAR Odometry Estimation with Trainable Local Geometric Features
Kohei Honda, Kenji Koide, Masashi Yokozuka +2
This paper presents a LiDAR odometry estimation framework called Generalized LOAM. Our proposed method is generalized in that it can seamlessly fuse various local geometric shapes…
Scalable Fiducial Tag Localization on a 3D Prior Map via Graph-Theoretic Global Tag-Map Registration
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents an accurate and scalable method for fiducial tag localization on a 3D prior environmental map. The proposed method comprises three steps: 1) visual odometry-bas…
Globally Consistent and Tightly Coupled 3D LiDAR Inertial Mapping
Kenji Koide, Masashi Yokozuka, Shuji Oishi +1
This paper presents a real-time 3D mapping framework based on global matching cost minimization and LiDAR-IMU tight coupling. The proposed framework comprises a preprocessing modul…
Globally Consistent 3D LiDAR Mapping with GPU-accelerated GICP Matching Cost Factors
Kenji Koide, Masashi Yokozuka, Shuji Oishi +1
This paper presents a real-time 3D LiDAR mapping framework based on global matching cost minimization. The proposed method constructs a factor graph that directly minimizes matchin…
Adaptive Hyperparameter Tuning for Black-box LiDAR Odometry
Kenji Koide, Masashi Yokozuka, Shuji Oishi +1
This study proposes an adaptive data-driven hyperparameter tuning framework for black-box 3D LiDAR odometry algorithms. The proposed framework comprises offline parameter-error fun…
4D Attention: Comprehensive Framework for Spatio-Temporal Gaze Mapping
Shuji Oishi, Kenji Koide, Masashi Yokozuka +1
This study presents a framework for capturing human attention in the spatio-temporal domain using eye-tracking glasses. Attention mapping is a key technology for human perceptual a…
LiTAMIN2: Ultra Light LiDAR-based SLAM using Geometric Approximation applied with KL-Divergence
Masashi Yokozuka, Kenji Koide, Shuji Oishi +1
In this paper, a three-dimensional light detection and ranging simultaneous localization and mapping (SLAM) method is proposed that is available for tracking and mapping with 500--…
VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points
Masashi Yokozuka, Shuji Oishi, Thompson Simon +1
In this paper, we propose a novel indirect monocular SLAM algorithm called "VITAMIN-E," which is highly accurate and robust as a result of tracking extremely dense feature points.…