Efficient implementation of the graph-based SLAM on an OMAP processor

An autonomous robot has to localize itself in an unknown area. Simultaneous Localization and Mapping (SLAM) allows for a robot to build a map of an unknown environment and localize simultaneously itself on this map. Graph-based SLAM methods use a graph to represent and solve the SLAM problem. This paper presents an optimized implementation of the incremental 3D graph-based SLAM on an OMAP architecture used as open multimedia applications platform. This implementation uses an optimized data structure and an efficient memory access management to solve the nonlinear least squares problem related to the algorithm. It takes also advantage of the multi-core architecture to parallelize the algorithm. To evaluate our implementation, we will evaluate the processing times of the implemented algorithm compared to those of the well known framework g²o.