Cooperative localization for disconnected sensor networks and a mobile robot in friendly environments

• Published in: Information fusion 2017-09, Vol.37, p.22-36
• Main Authors: Yuan, Jing, Zhang, Jinhe, Ding, Song, Dong, Xingliang
• Format: Article
• Language: English
• Subjects: Cooperative localization; Mobile robot; Sensor information fusion; Sensor network
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2017.01.001

•An information fusion framework is proposed for robot-sensor network cooperative localization.•A parallax-based tracking method is designed for sensor nodes to localize the robot.•Information from both the robot's SLAM and nodes' target tracking is integrated closely.•Cooperative localization can achieve a better performance than the other methods. Localization for a disconnected sensor network is highly unlikely to be achieved by its own sensor nodes, since accessibility of the information between any pair of sensor nodes cannot be guaranteed. In this paper, a mobile robot (or a mobile sensor node) is introduced to establish correlations among sparsely distributed sensor nodes which are disconnected, even isolated. The robot and the sensor network operate in a friendly manner, in which they can cooperate to perceive each other for achieving more accurate localization, rather than trying to avoid being detected by each other. The mobility of the robot allows for the stationary and internally disconnected sensor nodes to be dynamically connected and correlated. On one hand, the robot performs simultaneous localization and mapping (SLAM) based on the constrained local submap filter (CLSF). The robot creates a local submap composed of the sensor nodes present in its immediate vicinity. The locations of these nodes and the pose (position and orientation angle) of the robot are estimated within the local submap. On the other hand, the sensor nodes in the submap estimate the pose of the robot. A parallax-based robot pose estimation and tracking (PROPET) algorithm, which uses the relationship between two successive measurements of the robot's range and bearing, is proposed to continuously track the robot's pose with each sensor node. Then, tracking results of the robot's pose from different sensor nodes are fused by the Kalman filter (KF). The multi-node fusion result are further integrated with the robot's SLAM result within the local submap to achieve more accurate localization for the robot and the sensor nodes. Finally, the submap is projected and fused into the global map by the CLSF to generate localization results represented in the global frame of reference. Simulation and experimental results are presented to show the performances of the proposed method for robot-sensor network cooperative localization. Especially, if the robot (or the mobile sensor node) has the same sensing ability as the stationary sensor nodes, the localization accuracy can be signi