WALK-VIO: Walking-motion-Adaptive Leg Kinematic Constraint Visual-Inertial Odometry for Quadruped Robots

TL;DR

WALK-VIO introduces a walking-motion-adaptive leg kinematic constraint method for quadruped robot localization, improving robustness and accuracy of visual-inertial odometry across various walking gaits and outdoor environments.

Contribution

The paper presents a novel VIO approach that dynamically adjusts leg kinematic constraints based on walking motion, addressing limitations of previous methods that used fixed constraints.

Findings

WALK-VIO outperforms existing algorithms in accuracy across different gaits.

The adaptive constraint factor improves robustness in outdoor environments.

Created and published datasets for quadruped robot localization in simulation.

Abstract

In this paper, WALK-VIO, a novel visual-inertial odometry (VIO) with walking-motion-adaptive leg kinematic constraints that change with body motion for localization of quadruped robots, is proposed. Quadruped robots primarily use VIO because they require fast localization for control and path planning. However, since quadruped robots are mainly used outdoors, extraneous features extracted from the sky or ground cause tracking failures. In addition, the quadruped robots' walking motion cause wobbling, which lowers the localization accuracy due to the camera and inertial measurement unit (IMU). To overcome these limitations, many researchers use VIO with leg kinematic constraints. However, since the quadruped robot's walking motion varies according to the controller, gait, quadruped robots' velocity, and so on, these factors should be considered in the process of adding leg kinematic constraints. We propose VIO that can be used regardless of walking motion by adjusting the leg kinematic constraint factor. In order to evaluate WALK-VIO, we create and publish datasets of quadruped robots that move with various types of walking motion in a simulation environment. In addition, we verified the validity of WALK-VIO through comparison with current state-of-the-art algorithms.