Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs

TL;DR

This paper introduces FILD++, a fast and incremental loop closure detection system using deep features and proximity graphs, achieving high accuracy and low latency in large-scale robotic mapping tasks.

Contribution

FILD++ combines a single neural network for global and local feature extraction with an incremental graph database, improving speed and accuracy over previous methods.

Findings

Achieves highest recall on 8 out of 11 datasets.

Runs in 22.05 ms on large datasets with 52,480 images.

Outperforms state-of-the-art approaches in accuracy and speed.

Abstract

In recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications.This article proposes an appearance-based loop closure detection pipeline named ``FILD++" (Fast and Incremental Loop closure Detection).First, the system is fed by consecutive images and, via passing them twice through a single convolutional neural network, global and local deep features are extracted.Subsequently, a hierarchical navigable small-world graph incrementally constructs a visual database representing the robot's traversed path based on the computed global features.Finally, a query image, grabbed each time step, is set to retrieve similar locations on the traversed route.An image-to-image pairing follows, which exploits local features to evaluate the spatial information. Thus, in the proposed article, we propose a single network for global and local feature extraction in contrast to our previous work (FILD), while an exhaustive search for the verification process is adopted over the generated deep local features avoiding the utilization of hash codes. Exhaustive experiments on eleven publicly available datasets exhibit the system's high performance (achieving the highest recall score on eight of them) and low execution times (22.05 ms on average in New College, which is the largest one containing 52480 images) compared to other state-of-the-art approaches.