This paper presents a new approach to the intra- localization among a team of robots working in constrained 3D space of urban environments. As the base formation, a team of three ground robots and one wall-climbing robot are deployed on ground and on a wall or ceiling, respectively. The three ground robots localize themselves using an existing panoramic vision-based method. However, no existing method can uniquely determine the pose of the climbing robot based on the positions of three ground robots in its image and in the world coordinate systems; up to four valid solutions could exist using known algorithms, although only one is genuine. By carefully examining these methods, two new algorithms for uniquely locating the climbing robot are proposed. The first algorithm makes use of the straight line constraint of robot motion and can uniquely determine the pose of climbing robot by moving the climbing robot straightly for two small steps. The second algorithm is based on the principle of Bayesian filter and take advantage of the motion sensor readings to loose the straight line constraint. The algorithm could continuously determine the climbing robot's pose after the initial pose is obtained. Extensive simulations are conducted to validate the soundness and robustness of our algorithms. Preliminary experiments are also carried out to examine the feasibility in applying these algorithms in real robot applications.
['Yi Feng', 'Zhigang Zhu', 'Jizhong Xiao']
Self-localization of a heterogeneous multi-robot team in constrained 3D space