Unmanned Vehicle Obstacle Orientation via 2D Point Cloud Projection

The patent segments the point cloud data processing into distinct stages: first projecting 3D point cloud data onto a 2D plane to create a projection graph, then performing gridding to divide it into discrete grid cells, and finally detecting straight lines within these grids. This segmentation transforms the complex 3D orientation detection into a series of simpler 2D processing steps, improving both computational efficiency and accuracy.

The patent applies dimensionality reduction by projecting three-dimensional point cloud data onto a two-dimensional plane. This transformation from 3D to 2D space simplifies the subsequent processing while preserving the essential orientation information needed for obstacle detection, effectively resolving the contradiction between processing simplicity and detection accuracy.

The patent introduces intermediate processing steps between raw point cloud data and final orientation determination. Specifically, it uses 2D projection as an intermediary representation and employs grid-based straight line detection as a mediating algorithm. These intermediaries transform the unreliable direct fitting approach into a more robust multi-stage process that reliably determines obstacle orientation.

The patent replaces the traditional mechanical fitting approach with a computational geometry-based method. Instead of directly fitting contours to point cloud data, it substitutes this with a systematic process of projection, gridding, and straight line detection, which provides more reliable and accurate orientation determination through mathematical rigor rather than approximate fitting.