Static-Map and Dynamic Object Reconstruction in Outdoor Scenes Using 3-D Motion Segmentation

Abstract

This letter aims to build the static-map of a dynamic scene using a mobile robot equipped with 3-D sensors. The sought static-map consists of only the static scene parts, which has a vital role in scene understanding and landmark based navigation. Building static-map requires the categorization of moving and static objects. In this work, we propose a Sparse subspace clustering-based motion segmentation method that categories the static scene parts and the multiple moving objects using their 3D motion trajectories. Our motion segmentation method uses the raw trajectory data, allowing the objects to move in direct 3-D space, without any projection model assumption or whatsoever. We also propose a complete pipeline for static-map building, which estimates the interframe motion parameters by exploiting the minimal 3-point random sample consensus algorithm on the feature correspondences only from the static scene parts. The proposed method has been especially designed and tested for large scene in real outdoor environments. On one hand, our 3-D motion segmentation approach outperforms its 2-D-based counterparts, for extensive experiments on KITTI dataset. On the other hand, separately reconstructed static-maps and moving objects for various dynamic scenes are very satisfactory.