Abstract
Mapping and localization are essential capabilities of robotic systems. Although the majority of mapping systems focus on static environments, the deployment in real-world situations requires them to handle dynamic objects. In this paper, we propose an approach for an RGB-D sensor that is able to consistently map scenes containing multiple dynamic elements. For localization and mapping, we employ an efficient direct tracking on the truncated signed distance function (TSDF) and leverage color information encoded in the TSDF to estimate the pose of the sensor. The TSDF is efficiently represented using voxel hashing, with most computations parallelized on a GPU. For detecting dynamics, we exploit the residuals obtained after an initial registration, together with the explicit modeling of free space in the model. We evaluate our approach on existing datasets, and provide a new dataset showing highly dynamic scenes. These experiments show that our approach often surpass other state-of-the-art dense SLAM methods. We make available our dataset with the ground truth for both the trajectory of the RGB-D sensor obtained by a motion capture system and the model of the static environment using a high-precision terrestrial laser scanner. Finally, we release our approach as open source code.
Abstract (translated)
测绘和定位是机器人系统的基本能力。尽管大多数映射系统都关注静态环境，但在实际情况下的部署需要它们处理动态对象。本文提出了一种能连续绘制包含多个动态元素的场景的RGB-D传感器方法。对于定位和映射，我们对截断有符号距离函数（TSDF）采用了有效的直接跟踪，并利用TSDF中编码的颜色信息来估计传感器的姿态。使用体素散列有效地表示TSDF，大多数计算在GPU上并行。为了检测动力学，我们利用初始配准后得到的残差以及模型中自由空间的显式建模。我们评估了现有数据集的方法，并提供了一个显示高度动态场景的新数据集。这些实验表明，我们的方法往往优于其他最先进的密集撞击方法。我们提供了我们的数据集，包括通过运动捕获系统获得的RGB-D传感器的轨迹和使用高精度地面激光扫描仪的静态环境模型的地面真值。最后，我们将我们的方法作为开源代码发布。
URL
https://arxiv.org/abs/1905.02082
