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Small obstacle detection using stereo vision for autonomous ground vehicleAuthor: Krishnam Gupta Date: 2018-12-19 Report no: IIIT/TH/2018/95 Advisor:Madhava Krishna AbstractSmall and medium sized obstacles such as rocks, small boulders, bricks left unattended on the road can pose hazards for autonomous as well as human driving situations. Many times these objects are too small on the road and go unnoticed on depth and point cloud maps obtained from state of the art range sensors such as 3D LIDAR. In the first part of thesis, a novel algorithm is proposed that fuses both appearance and 3D cues such as image gradients, curvature potentials and depth variance into a Markov Random Field (MRF) formulation that segments the scene into obstacle and non obstacle regions. Appearance and depth data obtained from a ZED stereo pair mounted on a Husky robot is used for this purpose. While identifying true positive obstacles such as rocks, large stones accurately our algorithm is simultaneously robust to false positive sources such as appearance changes on the road, papers and road markings. High accuracy detection in challenging scenes such as when the foreground obstacle blends with the background road scene vindicates the efficacy of the proposed formulation. In the second part of thesis, a novel neural network architecture called MergeNet for discovering small obstacles for on-road scenes in the context of autonomous driving is proposed. The basis of the architecture rests on the central consideration of training with less amount of data since the physical setup and the annotation process for small obstacles is hard to scale. For making effective use of the limited data, we propose a multi-stage training procedure involving weight-sharing, separate learning of low and high level features from the RGBD input and a refining stage which learns to fuse the obtained complementary features. The model is trained and evaluated on the Lost and Found dataset and is able to achieve state-of-art results with just 135 images in comparison to the 1000 images used by the previous benchmark. Additionally, we also compare our results with recent methods trained on 6000 images and show that our method achieves comparable performance with only 1000 training samples. Full thesis: pdf Centre for Robotics |
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