论文阅读：Driving on Point Clouds
《Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments》 引入 大规模户外环境的自主导航对机器人系统至关重要。 除了定位与地图构建之外，运动规划是任何自主导航系统的关键功能。在一般形式中，运动规划是计算连接
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相关文章
- 1. 论文阅读：Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
- 2. 【论文阅读】-2019-CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING
- 3. Ground Segmentation based on Loopy Belief Propagation for Sparse 3D Point Clouds （论文速读）
- 4. 论文阅读： Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
- 5. 【论文阅读】【综述】Deep Learning for 3D Point Clouds: A Survey
- 6. 论文阅读 KPConv: Flexible and Deformable Convolution for Point Clouds
- 7. 论文阅读之：Associatively Segmenting Instances and Semantics in Point Clouds
- 8. 论文《RandLaNet: Efficient Semantic Segmentation of Large-Scale Point Clouds》阅读笔记
- 9. 【论文阅读】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
- 10. 论文阅读：KPConv: Flexible and Deformable Convolution for Point Clouds