Robust and Efficient Relative Pose with a Multi-camera System for Autonomous Vehicle in Highly Dynamic Environments

TL;DR

This paper introduces a fast, robust 4-point algorithm for multi-camera relative pose estimation in highly dynamic environments, effectively handling numerous outliers for autonomous vehicle navigation.

Contribution

It presents a novel, efficient 4-point algorithm leveraging scene priors for multi-camera systems, capable of robustly estimating relative pose amidst many outliers.

Findings

Algorithm runs at about 0.5 microseconds per root

Effective in scenarios with overwhelming outliers

Validated on synthetic and real data

Abstract

This paper studies the relative pose problem for autonomous vehicle driving in highly dynamic and possibly cluttered environments. This is a challenging scenario due to the existence of multiple, large, and independently moving objects in the environment, which often leads to excessive portion of outliers and results in erroneous motion estimation. Existing algorithms cannot cope with such situations well. This paper proposes a new algorithm for relative pose using a multi-camera system with multiple non-overlapping individual cameras. The method works robustly even when the numbers of outliers are overwhelming. By exploiting specific prior knowledge of driving scene we have developed an efficient 4-point algorithm for multi-camera relative pose, which admits analytic solutions by solving a polynomial root-finding equation, and runs extremely fast (at about 0.5$u$s per root). When the solver is used in combination with RANSAC, we are able to quickly prune unpromising hypotheses, significantly improve the chance of finding inliers. Experiments on synthetic data have validated the performance of the proposed algorithm. Tests on real data further confirm the method's practical relevance.