Limited environmental information path planning based on 3D point cloud reconstruction

We present a new limited environmental information path planning procedure (IEIPPP) that finds collision-free paths without prior knowledge of feasible paths or obstacle locations by analyzing an image set of the area of interest. IEIPPP uses COLMAP to process the image set and reconstruct a three-dimensional point cloud model of the environment. Then, mechanical selective rapidly exploring random tree star is used to find the required path on the point cloud model. Gravitation and repulsion are introduced to correct the positions of random nodes and reduce the collision probability, and an elastic potential energy calculation is introduced to balance the height difference between adjacent nodes and stabilize vertical fluctuation of the path. To reduce computational cost and running time, a target-based sampling strategy is used to enable selective sampling. We evaluate IEIPPP with different image datasets and show that it can identify a collision-free path without other sensor equipment.