Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge

TL;DR

This paper introduces a map-free visual relocalization method that leverages instance and depth knowledge to improve matching accuracy and scale recovery, addressing key challenges in autonomous navigation and AR.

Contribution

It proposes a novel approach combining instance-based matching and depth estimation to enhance relocalization accuracy without relying on pre-built maps.

Findings

Significantly reduces mismatching across different objects.

Improves scale recovery accuracy from a single image.

Demonstrates superior performance in real-world scenarios.

Abstract

Map-free relocalization technology is crucial for applications in autonomous navigation and augmented reality, but relying on pre-built maps is often impractical. It faces significant challenges due to limitations in matching methods and the inherent lack of scale in monocular images. These issues lead to substantial rotational and metric errors and even localization failures in real-world scenarios. Large matching errors significantly impact the overall relocalization process, affecting both rotational and translational accuracy. Due to the inherent limitations of the camera itself, recovering the metric scale from a single image is crucial, as this significantly impacts the translation error. To address these challenges, we propose a map-free relocalization method enhanced by instance knowledge and depth knowledge. By leveraging instance-based matching information to improve global matching results, our method significantly reduces the possibility of mismatching across different objects. The robustness of instance knowledge across the scene helps the feature point matching model focus on relevant regions and enhance matching accuracy. Additionally, we use estimated metric depth from a single image to reduce metric errors and improve scale recovery accuracy. By integrating methods dedicated to mitigating large translational and rotational errors, our approach demonstrates superior performance in map-free relocalization techniques.