  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.
