Deep Dense Local Feature Matching and Vehicle Removal for Indoor Visual Localization

TL;DR

This paper introduces a robust indoor visual localization method using deep dense local feature matching and vehicle removal, achieving high accuracy in challenging parking lot environments with similar vehicle appearances.

Contribution

It presents a novel framework combining deep local features and vehicle detection to improve indoor localization accuracy in vehicle-rich, low-texture scenes.

Findings

Achieves 86.9% localization accuracy on benchmark dataset.

Outperforms existing methods in vehicle-rich indoor scenes.

Robust to low-texture environments and false matches.

Abstract

Visual localization is an essential component of intelligent transportation systems, enabling broad applications that require understanding one's self location when other sensors are not available. It is mostly tackled by image retrieval such that the location of a query image is determined by its closest match in the previously collected images. Existing approaches focus on large scale localization where landmarks are helpful in finding the location. However, visual localization becomes challenging in small scale environments where objects are hardly recognizable. In this paper, we propose a visual localization framework that robustly finds the match for a query among the images collected from indoor parking lots. It is a challenging problem when the vehicles in the images share similar appearances and are frequently replaced such as parking lots. We propose to employ a deep dense local feature matching that resembles human perception to find correspondences and eliminating matches from vehicles automatically with a vehicle detector. The proposed solution is robust to the scenes with low textures and invariant to false matches caused by vehicles. We compare our framework with alternatives to validate our superiority on a benchmark dataset containing 267 pre-collected images and 99 query images taken from 34 sections of a parking lot. Our method achieves 86.9 percent accuracy, outperforming the alternatives.