Real-time Obstacle Map Building with Target Tracking

In this paper, a new method is proposed to build a probabilistic occupancy map for an unmanned aerial vehicle (UAV) equipped with a forward-looking sensor, such as a laser scanning sensor (known as lidar). For a UAV, target tracking as well as mapping of obstacles are both important. Instead of using raw measurements to build a map, the proposed algorithm uses the interacting multiple model (IMM)-based target formulation and tracking method first to process the noisy measurement data. The state estimates and true target probability of each point-mass target tracks are then used to build a probabilistic occupancy map. Therefore, simultaneous tracking and mapping of both moving and stationary obstacles are accomplished in real time. In addition, the mapping algorithm has the robustness to the noisy sensor measurements. The obtained probabilistic occupancy map shows good agreement with the physical layout of the obstacles in the field in simulations. This shows the potential that the developed method can be used to help an unmanned vehicle navigate the field without a previous database of obstacles.