Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM
Abstract
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.
Keywords
Cite
@article{arxiv.2306.06850,
title = {Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM},
author = {Peter Stratton and Sandilya Sai Garimella and Ashwin Saxena and Nibarkavi Amutha and Emaad Gerami},
journal= {arXiv preprint arXiv:2306.06850},
year = {2023}
}