Feature based SLAM using laser sensor data with maximized information usage

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 2011 IEEE International Conference on Robotics and Automation, 2011, pp. 1811 - 1816
Issue Date:
2011-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010006675OK.pdf Published version3.16 MB
Adobe PDF
This paper formulates the SLAM problem using 2D laser data as an optimization problem. The environment is modeled as a set of curves and the variables of the optimization problem are the robot poses as well as the parameters describing the curves. There are two key differences between this SLAM formulation and existing SLAM methods. First, the environment is represented by continuous curves instead of point clouds or occupancy grids. Second, all the laser readings, including laser beams which returns its maximum range value, have been included in the objective function. As the objective function to be optimized contains discontinuities, it can not be solved by standard gradient based approaches and thus a Genetic Algorithm (GA) based method is applied. Matching of laser scans acquired from relatively far apart robot poses is achieved by applying GA on top of the Iterative closest point (ICP) algorithm. The new SLAM formulation and the use of a global optimization algorithm successfully avoid the convergence to local minimum for both the scan matching and the SLAM problem. Both simulated and experimental data are used to demonstrate the effectiveness of the proposed techniques.
Please use this identifier to cite or link to this item: