Task Optimization for a Class of Mobile Depth Sensor Network in Unknown Environments

This article proposes an optimization based on guaranteed coverage to address the challenge of continuously optimizing the mobile depth sensor network online due to the insufficient information captured in an unknown environment. In this optimization framework, the coverage set representing the coverage area of the depth sensor is introduced. A novel concept of the guaranteed coverage set of depth sensor is proposed, which is a subset of the coverage set. The proposed guaranteed coverage set, plays a crucial role in addressing measurement noises and enabling proper selection of dithers in optimization algorithms to stay within the coverage area. By utilizing the Luus-Jaakola (LJ) algorithm, the optimal pose for each depth sensor in the mobile network is determined. This takes into account the task-relevant cost function at every time instant. This algorithm ensures that the chosen poses effectively optimize the overall performance of the network. A method for the selection of the parameters in the proposed algorithm is also presented. The proposed optimization is then applied to a path planning case with obstacle avoidance, and a simultaneous mapping and coverage case, using stereo cameras as the depth sensors. Simulation and experiment results verify the effectiveness of the optimization for mobile depth sensor network in unknown environment.