Loop closing in SLAM using ant colony optimization

Loop closing in SLAM using ant colony optimization

Kupers, B.A.

Babuska, R. (mentor)
Prevel, R.G. (mentor)

Mechanical, Maritime and Materials Engineering

Delft Center for Systems and Control

2013-05-27

For service robots to be employed in normal human environments, autonomous navigation is an important requirement. To enable autonomous navigation, the robot needs to be able to build a map of any unknown environment. The problem of letting a robot build a map and simultaneously localizing itself within the same map is known in robotics as the Simultaneous Localization and Mapping (SLAM) problem. An important problem in SLAM is known as the loop closing problem. This problem occurs when the trajectory of the robot contains a large loop. After traversing this loop, the accumulated pose error causes the algorithm to fail to recognize the robot has returned to its original position. In this thesis a novel algorithm is introduced to improve the loop closing behaviour of a widely used SLAM algorithm, called FastSLAM. FastSLAM uses a particle filter to estimate the pose of the robot. The resampling step in the particle filter algorithm causes particle depletion, which inhibits correct loop closures. The proposed algorithm, called Ant Colony Optimization (ACO-)SLAM, uses ACO to improve the resampling step of the FastSLAM algorithm. ACO-SLAM does this by incorporating a measure of the map consistency and a measure for correct loop closures into the resampling step. Simulations are used to compare the performance of both algorithms with varying noise settings. Robby, the personal robot developed by the Delft Biorobotics lab, is used for experiments to test the performance of the algorithms in a realistic human environment.

http://resolver.tudelft.nl/uuid:08b52bba-9453-42ba-b069-49232980d5df

Student theses

master thesis

(c) 2013 Kupers, B.A.