Toward evaluation of visual navigation algorithms on RGB-D data from the first- and second-generation Kinect
Toward evaluation of visual navigation algorithms on RGB-D data from the first- and second-generation Kinect
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DOI：
10.1007/s00138-016-0802-6
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发表时间：
2017-02-01
影响因子：
3.3
通讯作者：
Skrzypczynski, Piotr
中科院分区：
文献类型：
--
作者：
Kraft, Marek;Nowicki, Michal;Skrzypczynski, Piotr
尽管商用RGB-D传感器的引入使移动机器人的视觉导航方法取得了重大进展，但基于结构光的传感器，如微软Kinect和华硕Xtion Pro Live，在范围、视野和深度测量精度方面存在一些重要的局限性。最近推出的基于飞行时间测量原理的第二代Kinect，为机器人和计算机视觉研究人员带来了一种克服这些限制的传感器。然而，就像旧的Kinect一样，新Kinect是为电脑游戏和人类动作捕捉而不是导航而设计的，目前还不清楚导航方法，如视觉里程计和SLAM，能从改进的参数中受益多少。虽然有许多公开可用的RGB-D数据集，但只有少数数据集提供了评估导航方法所需的地面真实信息，而且据我们所知，它们都不包含新版本Kinect注册的序列。因此，本文描述了一个新的RGB-D数据集，这是第一次尝试系统地评估室内导航算法在相同环境下沿着相同轨迹的两个不同传感器的数据。该数据集包含来自传感器的同步RGB-D帧和来自基于分布式摄像机的外部运动捕捉系统的适当地面真相。我们详细描述了数据配准过程，然后在获得的序列上评估了我们的RGB-D视觉里程计算法，研究了两种传感器的特定属性和局限性如何影响该导航方法的性能。
Although the introduction of commercial RGB-D sensors has enabled significant progress in the visual navigation methods for mobile robots, the structured-light-based sensors, like Microsoft Kinect and Asus Xtion Pro Live, have some important limitations with respect to their range, field of view, and depth measurements accuracy. The recent introduction of the second- generation Kinect, which is based on the time-of-flight measurement principle, brought to the robotics and computer vision researchers a sensor that overcomes some of these limitations. However, as the new Kinect is, just like the older one, intended for computer games and human motion capture rather than for navigation, it is unclear how much the navigation methods, such as visual odometry and SLAM, can benefit from the improved parameters. While there are many publicly available RGB-D data sets, only few of them provide ground truth information necessary for evaluating navigation methods, and to the best of our knowledge, none of them contains sequences registered with the new version of Kinect. Therefore, this paper describes a new RGB-D data set, which is a first attempt to systematically evaluate the indoor navigation algorithms on data from two different sensors in the same environment and along the same trajectories. This data set contains synchronized RGB-D frames from both sensors and the appropriate ground truth from an external motion capture system based on distributed cameras. We describe in details the data registration procedure and then evaluate our RGB-D visual odometry algorithm on the obtained sequences, investigating how the specific properties and limitations of both sensors influence the performance of this navigation method.