論文閱讀：Driving on Point Clouds
《Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments》 引入 大規模戶外環境的自主導航對機器人系統至關重要。 除了定位與地圖構建之外，運動規劃是任何自主導航系統的關鍵功能。在一般形式中，運動規劃是計算連接
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相關文章
- 1. 論文閱讀：Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
- 2. 【論文閱讀】-2019-CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING
- 3. Ground Segmentation based on Loopy Belief Propagation for Sparse 3D Point Clouds （論文速讀）
- 4. 論文閱讀： Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
- 5. 【論文閱讀】【綜述】Deep Learning for 3D Point Clouds: A Survey
- 6. 論文閱讀 KPConv: Flexible and Deformable Convolution for Point Clouds
- 7. 論文閱讀之：Associatively Segmenting Instances and Semantics in Point Clouds
- 8. 論文《RandLaNet: Efficient Semantic Segmentation of Large-Scale Point Clouds》閱讀筆記
- 9. 【論文閱讀】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
- 10. 論文閱讀：KPConv: Flexible and Deformable Convolution for Point Clouds