Pose Interpolation for Laser‐based Visual Odometry
Pose Interpolation for Laser‐based Visual Odometry
复制标题
基于激光的视觉里程计的姿态插值
DOI：
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发表时间：
2014
期刊：
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通讯作者：
T. Barfoot
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文献类型：
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作者：
Chi Hay Tong;S. Anderson;Hang Dong;T. Barfoot
在本文中，我们提出了两种方法获得视觉里程（VO）估计使用扫描激光测距仪。虽然常见的VO实现利用立体相机成像，但无源相机依赖于环境光。相比之下，主动照明传感器（如激光测距仪）可在各种照明条件下工作，包括完全黑暗。我们通过将基于稀疏外观的方法应用于激光强度图像来利用先前的成功，并且通过考虑在每个图像中检测到的兴趣点的时间戳来解决运动失真问题。为了说明唯一的时间戳，我们引入两个估计公式。在第一种方法中，我们通过引入一种新的帧到帧线性插值方案来扩展传统的离散时间批量估计公式，而在第二种方法中，我们从连续时间过程模型开始考虑估计问题。这是由高斯过程高斯牛顿（GPGN），一种非参数，连续时间，非线性，批量状态估计算法促进的。这两种基于激光的VO方法进行了比较，并使用两个实验配置获得的数据集进行验证。这些数据集包括由高帧率扫描激光雷达收集的11公里的现场数据和使用扫描平面激光测距仪进行的365米导线测量。统计分析显示，在高帧率场景下，线性插值的平均翻译误差占移动距离的百分比为5.3%，GPGN为4.4%。
In this paper, we present two methods for obtaining visual odometry (VO) estimates using a scanning laser rangefinder. Although common VO implementations utilize stereo camera imagery, passive cameras are dependent on ambient light. In contrast, actively illuminated sensors such as laser rangefinders work in a variety of lighting conditions, including full darkness. We leverage previous successes by applying sparse appearance‐based methods to laser intensity images, and we address the issue of motion distortion by considering the timestamps of the interest points detected in each image. To account for the unique timestamps, we introduce two estimator formulations. In the first method, we extend the conventional discrete‐time batch estimation formulation by introducing a novel frame‐to‐frame linear interpolation scheme, and in the second method, we consider the estimation problem by starting with a continuous‐time process model. This is facilitated by Gaussian process Gauss‐Newton (GPGN), an algorithm for nonparametric, continuous‐time, nonlinear, batch state estimation. Both laser‐based VO methods are compared and validated using datasets obtained by two experimental configurations. These datasets consist of 11 km of field data gathered by a high‐frame‐rate scanning lidar and a 365 m traverse using a sweeping planar laser rangefinder. Statistical analysis shows a 5.3% average translation error as a percentage of distance traveled for linear interpolation and 4.4% for GPGN in the high‐frame‐rate scenario.