An Active View Planning Method for Mobile Robots Using a Trinocular Visual Sensor

The ability of mobile robots to perceive and recognize environments is essential for autonomous navigation. To improve the performance of autonomous environment perception for mobile robots, it is important to effectively plan the next pose (position and orientation) of the sensor system at a current navigation state. In this paper, we propose a next-view-planning method for autonomous map construction needed for mobile robots with visual range sensor systems. The proposed view-planning method mimics the decision-making method of human beings, and uses the occlusion information reduced from the geometric relationship between the sensor view and objects as an important clue for the next sensor view planning. The proposed view-planning algorithms are developed in the following steps: 1) Given a prior map and range measurements sensed at a current location of the mobile robot, it is determined which parts in the map are interested in a view of solving the map uncertainty. 2) Based on the selected potential regions, some candidate poses of the sensor system for the next environment sensing are carefully generated. 3) The created candidates are evaluated by using a specially designed evaluation parameter, and the best one of them is selected as a next sensor position based on a fuzzy decision-making method. In this work, the principle of the view planning method is described in detail, and a series of experimental tests is performed to show the feasibility of the method for autonomous map building. For sensing the environments, an active trinocular vision sensor using laser structured light is utilized, which is mounted on the pan-tilt mechanism of the mobile robot, which is composed of a laser stripe projector and two cameras.