Fast Visible Trajectory Spatial Analysis in 3D Urban Environments Based on Local Point Clouds Data

In this paper, we present a fast and efficient visible trajectory planning for unmanned vehicles in a 3D urban environment based on local point clouds data. Our trajectory planning method is based on a two-step visibility analysis in 3D urban environments using predicted visibility from point clouds data. The first step in our unique concept is to extract basic geometric shapes. We focus on three basic geometric shapes from point clouds in urban scenes: planes, cylinders and spheres, extracting these geometric shapes using efficient Random Sample Consensus (RANSAC) algorithms with a high success rate of detection. The second step is a prediction of these geometric entities in the next time step, formulated as states vectors in a dynamic system using Kalman Filter (KF). Our planner is based on the optimal time horizon concept as a leading feature of our greedy search method, making our local planner safer. We demonstrate our visibility and trajectory planning method in simulations, showing predicted trajectory planning in 3D urban environments based on real Light Detection and Ranging (LiDAR) point clouds data.