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Elevation maps are commonly used to represent the environment of mobilerobots and are instrumental for locomotion and navigation tasks. However, puregeometric information is insufficient for many field applications that requireappearance or semantic information, which limits their applicability to otherplatforms or domains. In this work, we extend a 2.5D robot-centric elevationmapping framework by fusing multi-modal information from multiple sources intoa popular map representation. The framework allows inputting data contained inpoint clouds or images in a unified manner. To manage the different nature ofthe data, we also present a set of fusion algorithms that can be selected basedon the information type and user requirements. Our system is designed to run onthe GPU, making it real-time capable for various robotic and learning tasks. Wedemonstrate the capabilities of our framework by deploying it on multiplerobots with varying sensor configurations and showcasing a range ofapplications that utilize multi-modal layers, including line detection, humandetection, and colorization.

MEM: Multi-Modal Elevation Mapping for Robotics and Learning