PartSLAM: Unsupervised Part-based Scene Modeling for Fast Succinct Map Matching

TL;DR

This paper introduces PartSLAM, an unsupervised, part-based scene modeling method for fast, succinct 2D map matching that significantly improves speed and data compactness using pattern discovery techniques.

Contribution

It presents a novel unsupervised approach for compact, part-based scene modeling and map matching, utilizing CPD and RVP techniques for efficient robot navigation.

Findings

Achieves successful map matching with significant speedup

Produces maps that are tens of times more compact

Demonstrates effectiveness on a public dataset

Abstract

In this paper, we explore the challenging 1-to-N map matching problem, which exploits a compact description of map data, to improve the scalability of map matching techniques used by various robot vision tasks. We propose a first method explicitly aimed at fast succinct map matching, which consists only of map-matching subtasks. These tasks include offline map matching attempts to find a compact part-based scene model that effectively explains each map using fewer larger parts. The tasks also include an online map matching attempt to efficiently find correspondence between the part-based maps. Our part-based scene modeling approach is unsupervised and uses common pattern discovery (CPD) between the input and known reference maps. This enables a robot to learn a compact map model without human intervention. We also present a practical implementation that uses the state-of-the-art CPD technique of randomized visual phrases (RVP) with a compact bounding box (BB) based part descriptor, which consists of keypoint and descriptor BBs. The results of our challenging map-matching experiments, which use a publicly available radish dataset, show that the proposed approach achieves successful map matching with significant speedup and a compact description of map data that is tens of times more compact. Although this paper focuses on the standard 2D point-set map and the BB-based part representation, we believe our approach is sufficiently general to be applicable to a broad range of map formats, such as the 3D point cloud map, as well as to general bounding volumes and other compact part representations.