Deformable Map Matching for Uncertain Loop-Less Maps

TL;DR

This paper introduces a deformable map matching method for loop-less maps in autonomous navigation, combining deep learning-based image retrieval with joint map deformation to improve alignment accuracy in challenging scenarios.

Contribution

It proposes a novel map matching approach that merges maps and deforms them jointly, overcoming limitations of traditional methods in loop-less trajectories.

Findings

Improves map matching accuracy in loop-less scenarios

Utilizes deep CNN features for initial hypotheses

Demonstrates effectiveness on NCLT dataset

Abstract

In the classical context of robotic mapping and localization, map matching is typically defined as the task of finding a rigid transformation (i.e., 3DOF rotation/translation on the 2D moving plane) that aligns the query and reference maps built by mobile robots. This definition is valid in loop-rich trajectories that enable a mapper robot to close many loops, for which precise maps can be assumed. The same cannot be said about the newly emerging autonomous navigation and driving systems, which typically operate in loop-less trajectories that have no large loop (e.g., straight paths). In this paper, we propose a solution that overcomes this limitation by merging the two maps. Our study is motivated by the observation that even when there is no large loop in either the query or reference map, many loops can often be obtained in the merged map. We add two new aspects to map matching: (1) image retrieval with discriminative deep convolutional neural network (DCNN) features, which efficiently generates a small number of good initial alignment hypotheses; and (2) map merge, which jointly deforms the two maps to minimize differences in shape between them. To realize practical computation time, we also present a preemption scheme that avoids excessive evaluation of useless map-matching hypotheses. To verify our approach experimentally, we created a novel collection of uncertain loop-less maps by utilizing the recently published North Campus Long-Term (NCLT) dataset and its ground-truth GPS data. The results obtained using these map collections confirm that our approach improves on previous map-matching approaches.