We present Multi-Layer Intensity Map, a novel 3D object representation for robot perception and autonomous navigation. Intensity maps 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 intensity maps can be used to simultaneously estimate obstacles' height, solidity/density, and opacity. We demonstrate that intensity maps' 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 robots. We observe significant increases in success rates to more than 50%, up to a 9.5% decrease in normalized trajectory length, and up to a 22.6% increase in the F-score compared to current navigation methods using other sensor modalities.

翻译：我们提出多层强度图（Multi-Layer Intensity Map），这是一种用于机器人感知与自主导航的新型三维物体表征方法。强度图由多个堆叠的二维栅格图层构成，每个图层均源自特定高度区间内反射点云强度数据。不同层级的强度图可用于同步估算物体的高度、密实度/密度及不透明性。我们证明，强度图能够有效区分室内外环境中可安全穿越的障碍物（如珠帘/软帘、柔韧高草）与必须避让的障碍物（如透明表面（玻璃墙）、灌木丛、树木等）。此外，为应对稠密环境中的狭窄通道与非实体障碍物穿越，我们提出一种自适应膨胀方法——基于障碍物密实度及机器人偏好速度方向，动态扩大强度图检测到的障碍物范围。通过真实环境中的Turtlebot与波士顿动力Spot机器人测试，我们验证了这种改进的导航能力。相较于采用其他传感器模态的现有导航方法，该方法在成功概率上提升超50%（最高达9.5%的归一化轨迹长度缩减与22.6%的F值提升）。