Creating Navigable Space from Sparse Noisy Map Points

Abstract

We present a framework for creating navigable space from sparse and noisy map points generated by sparse visual SLAM methods. Our method incrementally seeds and creates local convex regions free of obstacle points along a robot's trajectory. Then a dense version of point cloud is reconstructed through a map point regulation process where the original noisy map points are first projected onto a series of local convex hull surfaces, after which those points falling inside the convex hulls are culled. The regulated and refined map points allow human users to quickly recognize and abstract the environmental information. We have validated our proposed framework using both a public dataset and a real environmental structure, and our results reveal that the reconstructed navigable free space has small volume loss (error) comparing with the ground truth, and the method is highly efficient, allowing real-time computation and online planning.