Towards seamless localization in challenging environments via high-definition maps and multi-sensor fusions

In the realm of autonomous vehicles, shifting scenarios can lead to localization failures, due to challenges like GPS signal loss and structural degradation. To address these issues, our paper introduces a multi-sensor and high-definition (HD) map-based framework for resilient vehicle localization in challenging environments. The framework integrates GPS, LiDAR, and ultra-wideband (UWB) to provide GPS localization results, UWB range measurements, LiDAR odometry results, and range measurements from plane detection and HD maps. These observations are then transformed into diverse constraints of varying scales, which are further represented as factors. Enhanced by a self-calibration module, these factors are seamlessly fused within a factor graph framework to achieve robust vehicle localization. Specifically, in environments where GPS is available and structured, the proposed method fuses results from GPS positioning and LiDAR simultaneous localization and mapping (SLAM). In GPS-unavailable and structured scenarios, fusion involves LiDAR SLAM results and UWB ranging information. For GPS-unavailable and degenerated-structured environments, the fusion incorporates range measurements from LiDAR plane detection and UWB anchors. We validate the effectiveness of the proposed method through practical applications in diverse environments covering a total driving distance of approximately 20.54 km, including underground parking, tunnels, roads under urban viaducts, and typical urban roads.