Direct Sparse Odometry

DSO is a novel direct and sparse formulation for Visual Odometry. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry – represented as inverse depth in a reference frame – and camera motion. This is achieved in real time by omitting the smoothness prior used in other direct methods and instead sampling pixels evenly throughout the images. DSO does not depend on keypoint detectors or descriptors, thus it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on mostly white walls. The proposed model integrates a full photometric calibration, accounting for exposure time, lens vignetting, and non-linear response functions. We thoroughly evaluate our method on three different datasets comprising several hours of video. The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness.

没接触过这个东西,mark一下。

在机器人学与计算机视觉领域,视觉里程计是一个通过分析相关图像序列,来确定机器人位置和朝向的过程。 在导航系统中,里程计(odometry)是一种利用致动器的移动数据来估算机器人位置随时间改变量的方法。例如,测量轮子转动的旋转编码器设备。里程计总是会遇到精度问题,例如轮子的打滑就会导致产生机器人移动的距离与轮子的旋转圈数不一致的问题。当机器人在不光滑的表面运动时,误差是由多种因素混合产生的。由于误差随时间的累积,导致了里程计的读数随着时间的增加,而变得越来越不可靠。 视觉里程计是一种利用连续的图像序列来估计机器人移动距离的方法。视觉里程计增强了机器人在任何表面以任何方式移动时的导航精度。

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