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）——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，每个图层由对应特定高度区间的反射点云强度推导而来。通过不同强度图层，可同步估计障碍物的高度、坚实度/密度及不透明性。实验证明，强度图能有效区分室内外环境中可安全穿越的障碍物（如串珠帘幕、柔韧高草丛）与必须规避的障碍物（如玻璃幕墙等透明表面、灌木丛、树木等）。此外，为应对密集环境中的狭窄通道及非刚性障碍物穿越问题，我们提出一种自适应膨胀方法——根据障碍物坚实度及机器人偏好速度方向，动态调整强度图中障碍物的膨胀/放大区域。通过真实环境中的Turtlebot和Boston Dynamics Spot机器人实验，我们验证了该方法在狭窄密集场景中的导航性能提升。相比采用其他传感器模态的现有导航方法，本方法在成功率上提升超过50%、归一化轨迹长度降低9.5%、F-score最高提升22.6%。