Graph-based non-linear least squares optimization for visual place recognition in changing environments

TL;DR

This paper introduces a graph-based non-linear least squares framework for visual place recognition that leverages various structural information and constraints to improve localization accuracy in changing environments.

Contribution

It presents a novel graph-based approach that systematically exploits multiple sources of structural information and constraints for enhanced place recognition.

Findings

Performance improvements across multiple datasets

Effective integration of intra-set similarities and pose information

Versatility in online and offline recognition scenarios

Abstract

Visual place recognition is an important subproblem of mobile robot localization. Since it is a special case of image retrieval, the basic source of information is the pairwise similarity of image descriptors. However, the embedding of the image retrieval problem in this robotic task provides additional structure that can be exploited, e.g. spatio-temporal consistency. Several algorithms exist to exploit this structure, e.g., sequence processing approaches or descriptor standardization approaches for changing environments. In this paper, we propose a graph-based framework to systematically exploit different types of additional structure and information. The graphical model is used to formulate a non-linear least squares problem that can be optimized with standard tools. Beyond sequences and standardization, we propose the usage of intra-set similarities within the database and/or the query image set as additional source of information. If available, our approach also allows to seamlessly integrate additional knowledge about poses of database images. We evaluate the system on a variety of standard place recognition datasets and demonstrate performance improvements for a large number of different configurations including different sources of information, different types of constraints, and online or offline place recognition setups.