©2010 International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 26 22 Sensor Fusion of Laser & Stereo Vision Camera for Depth Estimation and Obstacle Avoidance Saurav Kumar Daya Gupta Sakshi Yadav Computer Engg. Deptt. HOD, Computer Engg. Deptt. Student, Electrical & Electronics Delhi Technological University Delhi Technological University Delhi Technological University ABSTRACT Laser Range Finders (LRF) have been widely used in the field of robotics to generate very accurate 2-D maps of environment perceived by Autonomous Mobile Robot. Stereo Vision devices on the other hand provide 3-D view of the surroundings with a range far much than of a LRF but at the tradeoff of accuracy. This paper demonstrates a technique of sensor fusion of information obtained from LRF and Stereovision camera systems to extract the accuracy and range of independents systems respectively. Pruning of the 3D point cloud obtained by the Stereo Vision Camera is done to achieve computational efficiency in real time environment, after which the point cloud model is scaled down to a 2-D vision map, to further reduce computational costs. The 2D map of the camera is fused with the 2D cost map of the LRF to generate a 2-D navigation map of the surroundings which in turn is passed as an occupancy grid to VFH+ for obstacle avoidance and path-planning. This technique has been successfully tested on „Lakshya‟- an IGV platform developed at Delhi College of Engineering in outdoor environments. Keywords Sensor fusion, Stereovision, Laser range finder, Obstacle avoidance, Navigation map, 3D point cloud and Robotics 1. INTRODUCTION There are a large number of sensors available which can be used to detect obstacles present in the immediate surroundings, for eg. sensors like sonar, lasers, stereo vision camera, etc are widely used for obstacle detection. Each sensor works in a different manner and has its own limitation and advantages. Due to its inherent limitations, a single sensor cannot give an accurate reconstruction of the surroundings and hence cannot be used by mobile robots for obstacle detection and accurate path planning. This gives rise to the concept of sensor fusion i.e. integration of data from different sensors for successful obstacle avoidance and path planning. Distance sensors like laser range finders have been used before reconstruction of real world surroundings of a robot [1]. They give very accurate and reliable output, but in case of obstacles like a chair or table or obstacles not lying in the plane of the laser, they fail to detect the whole obstacle. Also, laser data is very much affected by the pitch and roll of the vehicle. On the other hand, stereo vision camera is involved in the acquisition of images of the dynamic environment. Though it can perceive up to infinity, its field of view is narrower as compared to that of LRF‟s. Also, if only the camera system is used for obstacle detection the data obtained is inferior in quality and it increases the computation burden on the system. Sensor fusion with Laser and Camera has been accomplished before in [2] but the method focuses on generating 3-D maps of 2D Laser maps and then fusing it with stereovision 3D map, which adds to computational burden. [3] deals with long range obstacle detection on road for which laser range finder detects and tracks the obstacle and stereovision camera system reconfirms the laser data. Sensor fusion of sensors like stereovision and lidar systems have been used widely for autonomous vehicles [4] [5]. In this paper, we propose an algorithm which relies on the fusion of the 2D cost maps generated by laser data with the 2D cost maps generated from the 3D real world map by stereo vision camera systems, to create an Occupancy grid for obstacle detection and trajectory planning. The fusion of both is a challenging task but the output is commendable and quite efficient to make a system move autonomously in a complex, dynamic environment with safe path planning and obstacle collision avoidance. Section II deals with range sensors- Hokuyo Laser Scanner and BumbleBee StereoVision Camera and the generation of their respective 2D cost maps. Section III deals sensor data fusion to generate an occupancy grid map and subsequent path planning. IV section is about the Lakshya‟s mechanical design and our results obtained from experiments performed on „Lakshya‟. The paper is concluded in section V by discussing future works and applications in this field. 2. RANGE SENSORS A BumbleBee StereoVision camera by Point Grey Research, with two Sony 1/3” progressive scan CCDs and a resolution of 640x480 at 48FPS or 1024x768 at 18FPS, was also used in conjugation with the Hokuyo laser, on the Lakshya platform for stereo imaging of surroundings. Fig. 1 Image Sensing The LRF used in experimentation was Hokuyo‟s URG-04LX which has a range of 20mm to 4m. It has a 240 o scanning area with 0.36 o angular resolution. Laser beams strike off an object to determine its distance and direction. The scanning time is around 100msec/scan. Based on the position of objects around the robot a 2D map is generated.