Abstract: A system for generating a semantic 3D map, that includes at least one image capture device capable of capturing and transmitting digital frames of images; a temporal and unified panoptic segmentation module programmed, structed, and/or configured to receive the frames of images from the at least one image capture device and integrate a heuristic panoptic label fusion module with a loss function of a neural network to realize end-to-end panoptic segmentation; a geometric segmentation module programmed, structured and/or configured to receive the frames of images from the at least one image capture device and for discovering previously unseen scene elements, wherein at every frame, it generates a set of closed 2D regions and a set of corresponding 3D segments from a depth image; a segmentation refinement module programmed, structed, and/or configured to refine geometric labels using panoptic labels; and a 3D volumetric integration module programmed, structed, and/or configured to directly register each pixel of

Abstract: A tightly coupled fusion approach that dynamically consumes light detection and ranging (LiDAR) and sonar data to generate reliable and scalable indoor maps for autonomous robot navigation. The approach may be used for the ubiquitous deployment of indoor robots that require the availability of affordable, reliable, and scalable indoor maps. A key feature of the approach is the utilization of a fusion mechanism that works in three stages: the first LiDAR scan matching stage efficiently generates initial key localization poses; a second optimization stage is used to eliminate errors accumulated from the previous stage and guarantees that accurate large-scale maps can be generated; and a final revisit scan fusion stage effectively fuses the LiDAR map and the sonar map to generate a highly accurate representation of the indoor environment.