Multi Objective Mapping And Path Planning Using Visual Slam And Object Detection

Path planning of the autonomous robots is one of the crucial tasks that need to be achieved for mobile robots to navigate through the environment intelligently. The robot paths are typically planned utilizing map that is accessible at the time with a certain optimization objective such as to minimizing the travel distance, or time. This thesis proposes a multi-objective path planning approach by integrating Simultaneous Localization And Mapping (SLAM) with a graph based optimization approach and an object detection algorithm. The proposed approach aims not only to nd a path that minimizes travel distance but also to minimize the number of obstacles in the path to be followed. This thesis uses Visual SLAM (VSLAM) as the basis to generate graphs for global path planning. VSLAM generates a trajectory network which is usually in the form of a spare graph (if odometry based) or probabilistic relations on landmark estimates relative to the robot. An object detection algorithm is run in parallel to provide additional information on trajectory network graphs generated by the VSLAM, to be used in multi-objective path planning. The VSLAM, object detection, and path planning elds are typically studied independently, but this thesis links the these elds to solve the multi-objective path planning problem. The rst part of the thesis presents the connections and methodology on using the VSLAM and object detection to generate trajectory network graphs. The nodes are inserted to the graph when a new keyframe is needed in VSLAM. The distance travelled between the nodes is the rst criterion to minimize and is computed while traversing. In parallel to VSLAM, the object detection component quanti es the number of objects detected between the nodes. Only the pre-trained objects to detect are quanti ed and the trained objects in the thesis are cars and trucks. The number of objects are the two additional edge information added to the graph. Later in the thesis, the multi-objective path planning on the generated graphs is presented. The objective of path planning on graph is not just on minimizing the distance to travel but also on minimizing the number of cars and trucks it passes. The proposed design is tested using KITTI dataset which is specialized for autonomous driving and consists of many cars and trucks. The design is not limited to autonomous driving applications, but can be applied to other elds such as surveillance, rescuing, and many more with di erent objects to detect.