Correlation Filter of 2D Laser Scans For Indoor Environment

TL;DR

This paper introduces a novel correlation filter for 2D laser scans that reduces redundant data in indoor SLAM by dropping scans with no new information, improving efficiency while maintaining accuracy.

Contribution

The paper presents a new adaptive filter for 2D laser scans and an indoor corridor detector, enhancing data processing efficiency in indoor SLAM systems.

Findings

Dropped over 50% of scans in experiments

Filter adapts to robot speed and lidar characteristics

Indoor corridor detector applicable to various shapes

Abstract

Modern laser SLAM (simultaneous localization and mapping) and structure from motion algorithms face the problem of processing redundant data. Even if a sensor does not move, it still continues to capture scans that should be processed. This paper presents the novel filter that allows dropping 2D scans that bring no new information to the system. Experiments on MIT and TUM datasets show that it is possible to drop more than half of the scans. Moreover thepaper describes the formulas that enable filter adaptation to a particular robot with known speed and characteristics of lidar. In addition, the indoor corridor detector is introduced that also can be applied to any specific shape of a corridor and sensor.