An Inter-Vehicle Observations Approach to Cooperative SLAM

Abstract

In this paper, a cooperative simultaneous localization and mapping (CSLAM) approach is proposed. The main idea of the proposed approach is to initialize the map of each vehicle comprising the known initial positions of collaborating vehicles, and continuing the SLAM process considering these vehicles as dynamic features (landmarks). The inter-vehicle observations and shared control data are used to predict and update the dynamic features. The proposed approach is first elucidated mathematically based on extended Kalman filter-simultaneous localization and mapping (EKF-SLAM) solution, and then, the performance gain is evaluated using simulations conducted for unmanned surface vehicles (USVs) with radar sensors. Simulation results show that this approach provides noticeable improvement versus the single-vehicle SLAM (or Mono-SLAM) in terms of localization accuracy and mapping performance.