Incremental RANSAC for Online Relocation in Large Dynamic Environments

TL;DR

This paper introduces an incremental RANSAC algorithm designed for real-time online vehicle relocation in large, dynamic environments with many outliers, extending traditional RANSAC for improved robustness and efficiency.

Contribution

The paper presents a novel incremental RANSAC scheme that enhances online relocation capabilities in dynamic environments by efficiently handling outliers and limited computation time.

Findings

Successfully estimates self-position in real-time with high outlier contamination

Extends preemption RANSAC to incremental form for online use

Demonstrates robustness and efficiency in large dynamic environments

Abstract

Vehicle relocation is the problem in which a mobile robot has to estimate the self-position with respect to an a priori map of landmarks using the perception and the motion measurements without using any knowledge of the initial self-position. Recently, RANdom SAmple Consensus (RANSAC), a robust multi-hypothesis estimator, has been successfully applied to offline relocation in static environments. On the other hand, online relocation in dynamic environments is still a difficult problem, for available computation time is always limited, and for measurement include many outliers. To realize real time algorithm for such an online process, we have developed an incremental version of RANSAC algorithm by extending an efficient preemption RANSAC scheme. This novel scheme named incremental RANSAC is able to find inlier hypotheses of self-positions out of large number of outlier hypotheses contaminated by outlier measurements.