Line segment based range scan matching without pose information for indoor environments
A mobile robot exploring an unknown environment often needs to keep track of its pose through its sensors. Range scan matching is a way of computing the pose difference of a robot at two different locations on the navigation path by finding common features observed in range sensor readings recorded at these locations. In this thesis, we introduce a new algorithm which computes this pose difference by matching common line segments extracted from two laser range scans taken from two different but unknown poses. In this algorithm, matching is performed by exploiting invariant geometric relations among line segments. The use of line segments instead of range points also reduces the computational complexity of determining the pose difference between two distinct scans. Compared to other scan matching algorithms, our method presents a powerful means for global scan matching, map building, place recognition, loop closing and multirobot mapping, all in real-time.