用拓扑地图实现机器人高效导航，减少误差累积与资源消耗
NavTopo: Leveraging Topological Maps For Autonomous Navigation Of a Mobile Robot
- 基于拓扑图构建全链路导航系统，利用图结构降低计算开销
- 在大型室内仿真环境中，导航性能优于传统度量地图方法
- 适合对实时性与内存敏感的移动机器人场景使用
移动机器人自主导航需具备建图、定位、路径规划与跟踪能力。传统建图方法构建密集度量地图（如占用栅格），易受里程计误差累积影响，且在大环境中的内存与计算开销巨大。另一种方法是利用环境的拓扑特性（如位置间的邻接关系），拓扑地图更抗误差积累、资源消耗低，并因图结构稀疏而支持快速路径规划。基于此，我们提出 NavTopo——一种基于拓扑地图的完整导航流水线，采用两级路径规划策略。该流水线通过匹配神经网络描述子与输入点云的二维投影进行图内定位，相比度量地图和基于点云的拓扑方法，显著降低内存占用。我们在一个大型室内照片级真实感仿真环境中测试该方法，并与基于主流度量建图方法 RTAB-MAP 的方案进行对比。实验结果表明，我们的拓扑方法在导航性能上显著优于度量方法，同时保持了合理的导航效率。
原文摘要 · Abstract (English)
Autonomous navigation of a mobile robot is a challenging task which requires ability of mapping, localization, path planning and path following. Conventional mapping methods build a dense metric map like an occupancy grid, which is affected by odometry error accumulation and consumes a lot of memory and computations in large environments. Another approach to mapping is the usage of topological properties, e.g. adjacency of locations in the environment. Topological maps are less prone to odometry error accumulation and high resources consumption, and also enable fast path planning because of the graph sparsity. Based on this idea, we proposed NavTopo - a full navigation pipeline based on topological map and two-level path planning. The pipeline localizes in the graph by matching neural network descriptors and 2D projections of the input point clouds, which significantly reduces memory consumption compared to metric and topological point cloud-based approaches. We test our approach in a large indoor photo-relaistic simulated environment and compare it to a metric map-based approach based on popular metric mapping method RTAB-MAP. The experimental results show that our topological approach significantly outperforms the metric one in terms of performance, keeping proper navigational efficiency.
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