We present Multi-Layer Intensity Map, a novel 3D object representation for robot perception and autonomous navigation. They consist of multiple stacked layers of 2D grid maps each derived from reflected point cloud intensities corresponding to a certain height interval. The different layers of the intensity maps can be used to simultaneously estimate obstacles' height, solidity/density, and opacity. We demonstrate that they can help accurately differentiate obstacles that are safe to navigate through (e.g. beaded/string curtains, pliable tall grass), from ones that must be avoided (e.g. transparent surfaces such as glass walls, bushes, trees, etc.) in indoor and outdoor environments. Further, to handle narrow passages, and navigate through non-solid obstacles in dense environments, we propose an approach to adaptively inflate or enlarge the obstacles detected on intensity maps based on their solidity, and the robot's preferred velocity direction. We demonstrate these improved navigation capabilities in real-world narrow, dense environments using a real Turtlebot and Boston Dynamics Spot. We observe significant increases in success rates (up to 50%), a 9.55% decrease in trajectory length, and up to a 10.9% increase in the F-score compared to current navigation methods using other sensor modalities.

翻译：我们提出多层强度地图（Multi-Layer Intensity Map），一种用于机器人感知与自主导航的新型3D物体表征。该地图由多个堆叠的二维栅格图层构成，每一层均源自对应特定高度区间的反射点云强度信息。强度地图的不同层可同时用于估计障碍物的高度、密实度/密度以及不透明度。我们证明，在室内外环境中，该方法能有效区分可通过的安全障碍物（例如串珠/流苏帘、柔韧高草丛）与必须避让的障碍物（如玻璃墙、灌木、树木等透明表面）。此外，为应对狭窄通道并在密集环境中穿越非固态障碍物，我们提出一种基于障碍物密实度及机器人偏好速度方向的自适应膨胀方法，对强度地图中检测到的障碍物进行适应性扩展/放大。通过在真实环境下使用Turtlebot和波士顿动力Spot机器人在狭窄密集空间中的实验，我们展示了这些增强的导航能力。相较于采用其他传感器模态的现有导航方法，本方法在成功率（最高提升50%）、轨迹长度（减少9.55%）及F值（最高提升10.9%）方面均实现显著提升。