RGBiD-SLAM用于精确实时的定位和3D绘图
RGBiD-SLAM for Accurate Real-time Localisation and 3D Mapping
摘要：
在本文中，我们提出了一个完整的SLAM系统的RGB-D相机，即RGB-iD SLAM。所提出的方法是一种密集的直接SLAM方法，其主要特点是在密集对齐或关键帧融合的例程中使用逆深度参数化中的深度图。该系统由2个并行工作的CPU线程组成，它们共享GPU的使用，用于密集对齐和关键帧融合例程。第一线程是以帧速率操作的前端，其处理来自RGB-D传感器的每个传入帧以计算增量测距法并将其整合到遵循基于相干性的策略周期性改变的关键帧中。第二个线程是后端，它从前端接收关键帧。该线程负责根据关键帧的结构对其进行分段，使用Bags of Words对其进行描述，尝试使用先前的关键帧找到潜在的循环闭合，并在这种情况下执行姿势图优化以进行轨迹校正。此外，我们的系统允许使用未注册和注册的深度图来计算里程，允许使用RGB-D传感器的定制校准。因此，在本文中，我们还提出了一个详细的校准管道来计算特定RGB-D相机的定制校准。在TUM RGB-D基准数据集上使用我们的方法进行的实验表明，在许多序列中，结果的准确性优于最先进的方法。该守则已在https://github.com/dangut/RGBiD-SLAM网站上提供，供研究之用。
英文摘要：
In this paper we present a complete SLAM system for RGB-D cameras, namely RGB-iD SLAM. The presented approach is a dense direct SLAM method with the main characteristic of working with the depth maps in inverse depth parametrisation for the routines of dense alignment or keyframe fusion. The system consists in 2 CPU threads working in parallel, which share the use of the GPU for dense alignment and keyframe fusion routines. The first thread is a front-end operating at frame rate, which processes every incoming frame from the RGB-D sensor to compute the incremental odometry and integrate it in a keyframe which is changed periodically following a covisibility-based strategy. The second thread is a back-end which receives keyframes from the front-end. This thread is in charge of segmenting the keyframes based on their structure, describing them using Bags of Words, trying to find potential loop closures with previous keyframes, and in such case perform pose-graph optimisation for trajectory correction. In addition, our system allows is able to compute the odometry both with unregistered and registered depth maps, allowing to use customised calibrations of the RGB-D sensor. As a consequence in the paper we also propose a detailed calibration pipeline to compute customised calibrations for particular RGB-D cameras. The experiments with our approach in the TUM RGB-D benchmark datasets show results superior in accuracy to the state-of-the-art in many of the sequences. The code has been made available on-line for research purposes https://github.com/dangut/RGBiD-SLAM.
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。
