ICRA 2011 Paper Abstract


Paper TuP1-InteracInterac.27

Liu, Minjie (University of Technology,Sydney), Huang, Shoudong (University of Technology, Sydney), Dissanayake, Gamini (University of Technology Sydney)

Feature Based SLAM Using Laser Sensor Data with Maximized Information Usage

Scheduled for presentation during the Poster Sessions "Interactive Session II: Systems, Control and Automation" (TuP1-InteracInterac), Tuesday, May 10, 2011, 13:40−14:55, Hall

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 29, 2020

Keywords SLAM


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.



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