Cooperative Visual-Inertial Odometry

Abstract

This paper studies the problem of multi-robot cooperative visual-inertial localization where each robot is equipped with only a single camera and IMU. We develop two cooperative visual-inertial odometry (C-VIO) algorithms within the multi-state constraint Kalman filter (MSCKF) framework, in which each robot utilizes not only its own measurements but constraints of common features co-observed with its neighbors within the current sliding window in order to improve the localization accuracy. The first centralized-equivalent algorithm tracks the robot-to-robot cross correlations and prioritizes the pose accuracy while requiring full capacity communication among all the robots during update. The second distributed algorithm ignores the robot-to-robot cross correlations to obtain a scalable, robust and efficient fully distributed structure where each robot only keeps its own states and communicates with its neighbors, while a covariance intersection (CI)-based update strategy is leveraged to guarantee consistency. The proposed algorithms are validated extensively in both Monte-Carlo simulations and real-world datasets, and shown to be able to achieve better accuracy with competitive efficiency.