  This paper presents Volume-DROID, a novel approach for Simultaneous
Localization and Mapping (SLAM) that integrates Volumetric Mapping and
Differentiable Recurrent Optimization-Inspired Design (DROID). Volume-DROID
takes camera images (monocular or stereo) or frames from a video as input and
combines DROID-SLAM, point cloud registration, an off-the-shelf semantic
segmentation network, and Convolutional Bayesian Kernel Inference (ConvBKI) to
generate a 3D semantic map of the environment and provide accurate localization
for the robot. The key innovation of our method is the real-time fusion of
DROID-SLAM and Convolutional Bayesian Kernel Inference (ConvBKI), achieved
through the introduction of point cloud generation from RGB-Depth frames and
optimized camera poses. This integration, engineered to enable efficient and
timely processing, minimizes lag and ensures effective performance of the
system. Our approach facilitates functional real-time online semantic mapping
with just camera images or stereo video input. Our paper offers an open-source
Python implementation of the algorithm, available at
https://github.com/peterstratton/Volume-DROID.
