Volume-DROID: 基于DROID-SLAM的体积建图实时实现
Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM
AI总结：
针对SLAM的实时语义建图需求，提出Volume-DROID方法，融合DROID-SLAM与ConvBKI，通过点云生成和位姿优化实现低延迟的实时3D语义建图与精准定位，并开源了Python实现。
摘要：
本文提出Volume-DROID，这是一种同时定位与地图构建（SLAM）的新方法，整合了体积建图（Volumetric Mapping）与可微循环优化启发设计（Differentiable Recurrent Optimization-Inspired Design, DROID）。Volume-DROID以相机图像（单目或双目）或视频帧为输入，结合DROID-SLAM、点云配准、现成的语义分割网络以及卷积贝叶斯核推理（Convolutional Bayesian Kernel Inference, ConvBKI），生成环境的3D语义地图，并为机器人提供精准定位。本方法的核心创新在于实现DROID-SLAM与ConvBKI的实时融合，这一融合通过从RGB-深度帧生成点云并优化相机位姿来达成。该集成经过设计以支持高效及时的处理，最大程度降低延迟，确保系统的有效性能。本方法仅需相机图像或双目视频输入，即可实现功能性的实时在线语义建图。本文提供了该算法的开源Python实现，可在https://github.com/peterstratton/Volume-DROID获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
