Probabilistic Mapping for UAV with Point-Mass Target Detection

Abstract

In this paper, a new method of building a probabilistic occupancy map for an unmanned aerial vehicle (UAV) equipped with a laser scanning sensor is proposed. 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 a well-known Interacting Multiple Model (IMM) based target formulation and tracking method to flrst process the noisy measurement data. The outputs from this process are used to recursively build the probabilistic occupancy map. The beneflt of this algorithm is attaining high quality occupancy map in spite of noisy sensor measurements simultaneously with the multiple-target tracking. In simulation, the obtained probabilistic occupancy map shows good agreement with the physical layout of the obstacles in the fleld. This shows the potential that the developed method can be used to help the unmanned vehicle navigate the fleld without prior knowledge. For an unmanned aerial vehicle (UAV), it is essential to be able to identify a safe path and localize its own position to perform a mission. Also, in order to perform a collision avoidance maneuver, the tracking of moving targets is necessary. A forward looking sensor such as a radar or laser scanner can help a UAV detect moving or stationary obstacles, especially when ∞ying in an outdoor environment. There are many tracking algorithms based on the Kalman fllter specialized in tracking a moving target. However, it is also necessary to produce a two-dimensional map of stationary obstacles to plan a safe path for the UAV. The method proposed in this thesis can achieve the tracking of both moving and stationary obstacles in a computationally e‐cient manner. The proposed method uses the outputs from the point mass target-tracking algorithm to generate a map of stationary obstacles. Therefore, the complex data association of large measurements is solved only once. In this paper, a new approach to build the probabilistic occupancy grid map based on the existing target-tracking algorithm will be presented assuming that the position of the vehicle is known. Probabilistic occupancy grid mapping has been very popular for robotic navigation applications and many papers have dealt with methods to update the known or unknown map and localize the position of the robot based on the measurements from sensors such as sonar or laser scanners. 1{3 The map update law has been extended to fllter out the measurements associated with dynamic obstacles in Ref. 4,5. Recently, the probabilistic occupancy grid map was constructed using a quadtree based approach which facilitates access to the information in the map and manages the memory e‐ciently. 6,7 In this paper, the Kalman fllter based Interacting Multiple Model (IMM) technique is used to employ a measurement model and to recursively build the probabilistic map of the sensor fleld. The localization of the unmanned vehicle is not addressed in this paper since our research goal is to provide a fast and robust