Front‑Fusion Based Recognition Pipeline for High‑Precision Map Static Obstacle Detection

Mapping and localization are core components of high‑definition maps, requiring accurate depth information from LiDAR or stereo cameras combined with precise IMU‑GNSS‑camera‑LiDAR calibration (±5 px) to reduce semantic errors caused by image‑only perception.

During offline mapping, point‑cloud registration, trajectory‑based 3D reconstruction (GPU/Multi‑Thread ICP) and storage of calibrated sensor parameters (extrinsics, intrinsics, MGRS tile indices) enable the generation of large 3D point‑cloud blocks containing static obstacles, fused road surfaces and dynamic obstacle trails.

Static obstacles constitute the essential asset for absolute coordinate systems, supporting SLAM‑based mapping and real‑time recognition; however, mis‑detections (e.g., lamp post classified as tree) arise from missing depth cues, especially in challenging scenarios such as trucks, sound barriers, or fences.

Two fusion strategies are discussed. Back‑fusion treats each sensor as an independent perception unit and merges their results via voting or pipeline processing, while front‑fusion builds a single pipeline that extracts ROI and 3D OBB features from images and point clouds, then performs regression and classification using networks such as RPN + ROIAlign or YOLO.

The front‑fusion approach leverages existing computer‑vision techniques, pre‑trained models, and point‑cloud feature extraction to improve accuracy, though it remains sensitive to scale and pixel offsets; semi‑supervised learning is proposed to automatically annotate static obstacles by projecting calibrated LiDAR points onto image space and filling missing depth via semantic and optical‑flow cues.

Implementation can start from open‑source repositories on GitHub, using cloud resources (e.g., Google GCE) for training; the resulting model (e.g., a modified Mask‑RCNN) can be deployed in CCloudware or custom renderers for visualization.

In conclusion, a complete front‑fusion based recognition pipeline for high‑precision map static obstacle detection is presented, accompanied by references to related works and tools.