Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences

TL;DR

This paper introduces a robust monocular localization method for autonomous vehicles that uses sparse semantic maps and deep learning to improve pose estimation under occlusion and environmental changes.

Contribution

It presents a novel sparse semantic map-based approach that leverages 2D-3D point-line correspondences learned through deep neural networks for improved localization.

Findings

Outperforms state-of-the-art methods in accuracy

Robust against occlusion and appearance changes

Effective in diverse traffic scene conditions

Abstract

Vision-based localization in a prior map is of crucial importance for autonomous vehicles. Given a query image, the goal is to estimate the camera pose corresponding to the prior map, and the key is the registration problem of camera images within the map. While autonomous vehicles drive on the road under occlusion (e.g., car, bus, truck) and changing environment appearance (e.g., illumination changes, seasonal variation), existing approaches rely heavily on dense point descriptors at the feature level to solve the registration problem, entangling features with appearance and occlusion. As a result, they often fail to estimate the correct poses. To address these issues, we propose a sparse semantic map-based monocular localization method, which solves 2D-3D registration via a well-designed deep neural network. Given a sparse semantic map that consists of simplified elements (e.g., pole lines, traffic sign midpoints) with multiple semantic labels, the camera pose is then estimated by learning the corresponding features between the 2D semantic elements from the image and the 3D elements from the sparse semantic map. The proposed sparse semantic map-based localization approach is robust against occlusion and long-term appearance changes in the environments. Extensive experimental results show that the proposed method outperforms the state-of-the-art approaches.