Parameter Optimization for Loop Closure Detection in Closed Environments

TL;DR

This paper presents an automatic parameter optimization method for loop closure detection in closed environments, enabling autonomous robots to adapt without prior knowledge or expert tuning, demonstrated in real-world scenarios.

Contribution

The proposed approach automatically optimizes loop closure parameters using boundary traversal data, removing the need for prior environment information or expert knowledge.

Findings

Effective in real-world scenarios with limited sensors

No prior environment or robot model information required

Improves autonomous localization in closed environments

Abstract

Tuning parameters is crucial for the performance of localization and mapping algorithms. In general, the tuning of the parameters requires expert knowledge and is sensitive to information about the structure of the environment. In order to design truly autonomous systems the robot has to learn the parameters automatically. Therefore, we propose a parameter optimization approach for loop closure detection in closed environments which requires neither any prior information, e.g. robot model parameters, nor expert knowledge. It relies on several path traversals along the boundary line of the closed environment. We demonstrate the performance of our method in challenging real world scenarios with limited sensing capabilities. These scenarios are exemplary for a wide range of practical applications including lawn mowers and household robots.