Visual-Marker-Based Localization for Flat-Variation Scene

Abstract

Localization, an indispensable component in robotics and automation, encounters difficulties arising from appearance variations, resulting in inaccurate data associations. Semantic-based positioning mitigates these challenges by filtering out invalid data, such as moving vehicles and worn road markings. Building on this insight, we introduce a robust semantic visual localization system, which has been successfully deployed in real-world settings. The system employs neural networks to extract road markers and associate data with a semantic map. To enhance system reliability, we employ several data filters. These filters remove images that are prone to misrecognition or poorly processed by neural networks. We propose two techniques for vehicle state estimation. The first, utilizing the inverse perspective mapping (IPM) matrix, directly determines the vehicle’s central pose. The second technique derives the camera pose using the Perspective-N-Points (PnP) method and leverages external parameters to infer the vehicle’s central state. The wheel encoder, with its robust anti-noise capability, offers odometry in the absence of semantic information, enhancing the system’s resilience. The crux of our approach lies in distinct semantic strategies: utilizing lane lines for orientation and road markers exclusively for translation estimation. We also detail an automatic construction method for the semantic map, enhancing the system’s practicality. Experimental results indicate that the IPM method outperforms the PnP approach, leading to notably improved positioning accuracy. Additionally, the error distribution of the IPM method more closely aligns with a normal distribution compared to that of the PnP approach.