Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation

• URL: http://arxiv.org/abs/2212.13676v1
• Date: Wed, 28 Dec 2022 03:13:32 GMT
• Title: Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation
• Authors: Shikuan Xie, Ran Song, Yuenan Zhao, Xueqin Huang, Yibin Li and Wei Zhang
• Abstract summary: Circular Accessible Depth (CAD) is a robust traversability representation for an unmanned ground vehicle (UGV) We propose a neural network, namely CADNet, with an attention-based multi-frame point cloud fusion module to encode the spatial features from point clouds captured by LiDAR.
• Score: 21.559882149457895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present the Circular Accessible Depth (CAD), a robust traversability representation for an unmanned ground vehicle (UGV) to learn traversability in various scenarios containing irregular obstacles. To predict CAD, we propose a neural network, namely CADNet, with an attention-based multi-frame point cloud fusion module, Stability-Attention Module (SAM), to encode the spatial features from point clouds captured by LiDAR. CAD is designed based on the polar coordinate system and focuses on predicting the border of traversable area. Since it encodes the spatial information of the surrounding environment, which enables a semi-supervised learning for the CADNet, and thus desirably avoids annotating a large amount of data. Extensive experiments demonstrate that CAD outperforms baselines in terms of robustness and precision. We also implement our method on a real UGV and show that it performs well in real-world scenarios.

Related papers

• CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings [21.512025767558498]
  CADSpotting represents primitives through densely sampled points with attributes like coordinates and colors. We propose a novel Sliding Window Aggregation (SWA) technique, combining weighted voting and Non-Maximum Suppression (NMS) Experiments on FloorPlanCAD and LS-CAD datasets show that CADSpotting significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2024-12-10T10:22:17Z)
• Constraint Learning for Parametric Point Cloud [19.190053031718417]
  Parametric point clouds are sampled from CAD shapes, and have become increasingly prevalent in industrial manufacturing. To address this issue, we analyzed the effect of constraints, and proposed its deep learning-friendly representation. CstNet is the first constraint-based learning method tailored for CAD shape analysis.
  arXiv  Detail & Related papers  (2024-11-12T12:18:18Z)
• FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud [7.711666704468952]
  We address the problem of traversability assessment using point clouds. We propose a pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume. We then propose a newtemporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds.
  arXiv  Detail & Related papers  (2024-06-24T12:01:55Z)
• Unsupervised Occupancy Learning from Sparse Point Cloud [8.732260277121547]
  Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities. In this paper, we propose a method to infer occupancy fields instead of Neural Signed Distance Functions. We highlight its capacity to improve implicit shape inference with respect to baselines and the state-of-the-art using synthetic and real data.
  arXiv  Detail & Related papers  (2024-04-03T14:05:39Z)
• Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer [53.413305467674434]
  We introduce open-source RGB data to support spike depth estimation, leveraging its annotations and spatial information. We propose a cross-modality cross-domain (BiCross) framework to realize unsupervised spike depth estimation. Our method achieves state-of-the-art (SOTA) performances, compared with RGB-oriented unsupervised depth estimation methods.
  arXiv  Detail & Related papers  (2022-08-26T09:35:20Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)
• 3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds [19.97270407211052]
  3D object tracking in point clouds is still a challenging problem due to the sparsity of LiDAR points in dynamic environments. We propose a Siamese voxel-to-BEV tracker, which can significantly improve the tracking performance in sparse 3D point clouds.
  arXiv  Detail & Related papers  (2021-11-08T12:47:11Z)
• Polyline Based Generative Navigable Space Segmentation for Autonomous Visual Navigation [57.3062528453841]
  We propose a representation-learning-based framework to enable robots to learn the navigable space segmentation in an unsupervised manner. We show that the proposed PSV-Nets can learn the visual navigable space with high accuracy, even without any single label.
  arXiv  Detail & Related papers  (2021-10-29T19:50:48Z)
• Voxel-based Network for Shape Completion by Leveraging Edge Generation [76.23436070605348]
  We develop a voxel-based network for point cloud completion by leveraging edge generation (VE-PCN) We first embed point clouds into regular voxel grids, and then generate complete objects with the help of the hallucinated shape edges. This decoupled architecture together with a multi-scale grid feature learning is able to generate more realistic on-surface details.
  arXiv  Detail & Related papers  (2021-08-23T05:10:29Z)
• A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries [62.28265459308354]
  Network learns end-to-end mapping between spatial positions and CFD quantities. Incompress laminar steady flow past a cylinder with various shapes for its cross section is considered. Network predicts the flow fields hundreds of times faster than our conventional CFD.
  arXiv  Detail & Related papers  (2020-10-15T12:15:02Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.