Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM

• URL: http://arxiv.org/abs/2306.06850v1
• Date: Mon, 12 Jun 2023 03:50:50 GMT
• Title: Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM
• Authors: Peter Stratton, Sandilya Sai Garimella, Ashwin Saxena, Nibarkavi Amutha, Emaad Gerami
• Abstract summary: Volume-DROID is a novel approach for Simultaneous localization and Mapping (SLAM) It combines DROID-SLAM, point cloud registration, an off-the-shelf semantic segmentation network, and Convolutional Bayesian Kernel Inference (ConvBKI) The key innovation of our method is the real-time fusion of DROID-SLAM and Convolutional Bayesian Kernel Inference (ConvBKI)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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