Abstract
The majority of approaches for acquiring dense 3D environment maps with RGB-D cameras assumes static environments or rejects moving objects as outliers. The representation and tracking of moving objects, however, has significant potential for applications in robotics or augmented reality. In this paper, we propose a novel approach to dynamic SLAM with dense object-level representations. We represent rigid objects in local volumetric signed distance function (SDF) maps, and formulate multi-object tracking as direct alignment of RGB-D images with the SDF representations. Our main novelty is a probabilistic formulation which naturally leads to strategies for data association and occlusion handling. We analyze our approach in experiments and demonstrate that our approach compares favorably with the state-of-the-art methods in terms of robustness and accuracy.
Abstract (translated)
使用RGB-D相机获取密集三维环境地图的大多数方法都假定静态环境或拒绝将移动对象作为离群值。然而，运动物体的表示和跟踪对于机器人或增强现实的应用具有重要的潜力。本文提出了一种新的密集对象层表示的动态冲击方法。我们在局部空间有符号距离函数（SDF）图中表示刚性物体，并用SDF表示将多目标跟踪表示为RGB-D图像的直接对准。我们的主要创新点是概率公式，它自然会导致数据关联和闭塞处理策略。我们在实验中分析了我们的方法，并证明了我们的方法在鲁棒性和准确性方面与最先进的方法相比具有优势。
URL
https://arxiv.org/abs/1904.11781
