A Non-Uniform Quadtree Map Building Method Including Dead-End Semantics Extraction

第一个工作，大家有时间可以去github点个小星星啦，and 希望大家不要喷啦，感谢感谢

摘要

Abstract:
To reduce the complexity of large-scene high-resolution maps while using the dead-end information distributed in the unmanned vehicle driving environment, we propose a novel non-uniform quadtree map-building method including dead-end semantic information extraction. By utilizing quadtree data structures, submaps and a positive-order tree depth organization approach, our proposed map can adapt to the large-scale high-resolution requirement and expand more easily to larger environments. To verify the practicality of our proposed map, we have successfully implemented map matching and path planning in real environments. Additionally, we effectively extract the dead-end semantic information that widely distributes in the environment, which can help unmanned vehicles avoid collisions and improve the search efficiency of the planning procedure. We evaluate our method with KITTI datasets, CARLA Simulator, and our self-collected real-world datasets. The experimental results show that our proposed method significantly reduces the complexity of large-scale high-resolution maps, effectively extracts dead-end semantic information, and has good practicality in real environments. The implementation of our method is released here: https://github.com/biter0088/Non-uniform-quadtree-map.
摘要。
为了降低大场景高分辨率地图的复杂度，同时利用无人车驾驶环境中分布的死角信息，我们提出了一种新型的包括死角语义信息提取的非均匀四叉树地图构建方法。通过利用四叉树数据结构、子图和正序树深度组织方法，我们提出的地图可以适应大规模高分辨率的要求，并更容易扩展到更大的环境。为了验证我们提出的地图的实用性，我们在实际环境中成功实现了地图匹配和路径规划。此外，我们有效地提取了广泛分布在环境中的死角语义信息，这可以帮助无人车避免碰撞并提高规划程序的搜索效率。我们用KITTI数据集、CARLA模拟器和我们自己收集的真实世界数据集来评估我们的方法。实验结果表明，我们提出的方法大大降低了大规模高分辨率地图的复杂性，有效地提取了死角语义信息，并在实际环境中具有良好的实用性。我们方法的实现在此发布： https://github.com/biter0088/Non-uniform-quadtree-map。

论文地址

论文代码(github)