1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
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…
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.…