Aoki Takanose

Tightly-Coupled Dynamic Object Tracking and RGB-D Inertial Odometry Estimation with Dual Quadrics

Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

Non-Parametric GNSS Integer Ambiguity Estimation via Positional Likelihood Field Marginalization

In this paper, we propose a non-parametric method for estimating the posterior distribution of global positioning satellite systems (GNSS) integer ambiguity. It is difficult to estimate the posterior probability of discrete integer ambiguities directly from carrier phase observations due to the uncl

Range-Based 6-DoF Monte Carlo SLAM with Gradient-Guided Particle Filter on GPU

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 robust in situations with high ambiguity, they are known to be unsuitable for high-dimensional problems due to the curs

Tightly-Coupled LiDAR-IMU-Leg Odometry With Online Learned Leg Kinematics Incorporating Foot Tactile Information

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 an online learning-based leg kinematics model named the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xli