Learning based semantic segmentation for robot navigation in outdoor environment
This paper describes an implementation of a mobile robot system for autonomous navigation in outdoor concurred walkways. The task was to navigate through non-modified pedestrian paths with people and bicycles passing by. The robot has multiple redundant sensors which include wheel encoders, an IMU, a DGPS and four laser scanner sensors. All the computation was done in a single laptop computer. A previously constructed map containing waypoints and landmarks for position correction is given to the robot. The robot system’s perception, road extraction and motion planning are detailed. The system was used and tested in a 1 km autonomous robot navigation challenge held in the City of Tsukuba, Japan named “Tsukuba Challenge 2007”. The proposed approach proved to be robust for outdoor navigation in cluttered and crowded walkways, first in campus paths and then running the challenge course multiple times between trials and the challenge final. The paper reports experimental results and overall performance of the system. Finally the lessons learned are discussed. The main contribution of this work is the report of a system integration approach for autonomous outdoor navigation and its evaluation.