A Joint 3D-2D based Method for Free Space Detection on Roads

In this paper, we address the problem of road segmentation and free space detection in the context of autonomous driving. Traditional methods either use 3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or stereo cameras or 2-dimensional (2D) cues such as lane markings, road boundaries and object detection. Typical 3D point clouds do not have enough resolution to detect fine differences in heights such as between road and pavement. Image based 2D cues fail when encountering uneven road textures such as due to shadows, potholes, lane markings or road restoration. We propose a novel free road space detection technique combining both 2D and 3D cues. In particular, we use CNN based road segmentation from 2D images and plane/box fitting on sparse depth data obtained from SLAM as priors to formulate an energy minimization using conditional random field (CRF), for road pixels classification. While the CNN learns the road texture and is unaffected by depth boundaries, the 3D information helps in overcoming texture based classification failures. Finally, we use the obtained road segmentation with the 3D depth data from monocular SLAM to detect the free space for the navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset, as well as videos captured by us, validate the superiority of the proposed approach over the state of the art.Comment: Accepted for publication at IEEE WACV 201

Divide and conquer: A hierarchical approach to large-scale structure-from-motion

In this paper we present a novel pipeline for large-scale SfM. We first organise the images into a hierarchical tree built using agglomerative clustering. The SfM problem is then solved by reconstructing smaller image sets and merging them into a common frame of reference as we move up the tree in a bottom-up fashion. Such an approach drastically reduces the computational load for matching image pairs without sacrificing accuracy. It also makes the resulting sequence of bundle adjustment problems well-conditioned at all stages of reconstruction. We use motion averaging followed by global bundle adjustment for reconstruction of each individual cluster. Our 3D registration or alignment of partial reconstructions based on epipolar relationships is both robust and reliable and works well even when the available camera-point relationships are poorly conditioned. The overall result is a robust, accurate and efficient pipeline for large-scale SfM. We present extensive results that demonstrate these attributes of our pipeline on a number of large-scale, real-world datasets and compare with the state-of-the-art. (C) 2017 Elsevier Inc. All rights reserved

Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning

Despite significant advances in recent years, structure-from-motion (SfM) pipelines suffer from two important drawbacks. Apart from requiring significant computational power to solve the large-scale computations involved, such pipelines sometimes fail to correctly reconstruct when the accumulated error in incremental reconstruction is large or when the number of 3D to 2D correspondences are insufficient. In this paper we present a novel approach to mitigate the above-mentioned drawbacks. Using an image match graph based on matching features we partition the image data set into smaller sets or components which are reconstructed independently. Following such reconstructions we utilise the available epipolar relationships that connect images across components to correctly align the individual reconstructions in a global frame of reference. This results in both a significant speed up of at least one order of magnitude and also mitigates the problems of reconstruction failures with a marginal loss in accuracy. The effectiveness of our approach is demonstrated on some large-scale real world data sets