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TJU_Lu 2024-11-30 23:45:34 +08:00
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@ -7,6 +7,11 @@ Video of this paper can be found: [YouTube](https://youtu.be/m7u1MYIuIn4), [bili
Some realworld experiment: [YouTube](https://youtu.be/LHvtbKmTwvE), [bilibili](https://www.bilibili.com/video/BV1jBpve5EkP) Some realworld experiment: [YouTube](https://youtu.be/LHvtbKmTwvE), [bilibili](https://www.bilibili.com/video/BV1jBpve5EkP)
<table>
<td align="center" style="border: none;"><img src="docs/realworld_1.gif" alt="Fig1" style="width: 100%;"></td>
<td align="center" style="border: none;"><img src="docs/realworld_2.gif" alt="Fig2" style="width: 100%;"></td>
</table>
## Introduction: ## Introduction:
We propose **a learning-based planner for autonomous navigation in obstacle-dense environments** which intergrats (i) perception and mapping, (ii) front-end path searching, and (iii) back-end optimization of classical methods into a single network. We propose **a learning-based planner for autonomous navigation in obstacle-dense environments** which intergrats (i) perception and mapping, (ii) front-end path searching, and (iii) back-end optimization of classical methods into a single network.

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