Vision based obstacle detection and map generation for reconnaissance

Obstacle detection and map generation is an essential tool for site reconnaissance applications. Further it enables optimal and efficient path planning for mobile agents to navigate in unknown environments. This paper proposes a solution to this problem through a stereo vision-based obstacle detection and depth measurement method for reconnaissance. The proposed approach employs a boundary tracing algorithm to identify the objects in the close vicinity to the vision system. Next, it generates a depth map that contains the obstacles and the distance to each obstacle with respect to the position of the mobile agent. An error model was developed to further improve the accuracy of the depth estimation. Depth maps from multiple angles for a given set of identified obstacles are superimposed in the same coordinate system to generate the aerial map. For objects with sharp edges, the aerial map was generated based on corners identified using a corner detection algorithm. A prototype of the system was implemented and tested in a controlled environment. The results show that percentage estimation error was significantly reduced after the depth calculation was refined using the proposed error model.