Direct Sparse Odometry
Direct Sparse Odometry
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DOI：
10.1109/tpami.2017.2658577
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发表时间：
2018-03-01
影响因子：
23.6
通讯作者：
Cremers, Daniel
中科院分区：
文献类型：
--
作者：
Engel, Jakob;Koltun, Vladlen;Cremers, Daniel
直接稀疏里程计（DSO）是一种视觉里程计方法的基础上，一种新颖的，高度精确的稀疏和直接的结构和运动公式。它结合了一个完全直接的概率模型（最大限度地减少光度误差）与一致的，联合优化的所有模型参数，包括几何表示为逆深度在参考帧和相机运动。这是在真实的时间实现的，通过省略在其他直接方法中使用的平滑度先验，而是在整个图像中均匀地采样像素。由于我们的方法不依赖于关键点检测器或描述符，因此它可以自然地从具有强度梯度的所有图像区域中采样像素，包括基本上无特征的墙壁上的边缘或平滑强度变化。建议的模型集成了一个完整的光度校准，占曝光时间，透镜渐晕，和非线性响应函数。我们在三个不同的数据集上彻底评估了我们的方法，这些数据集包括几个小时的视频。实验表明，所提出的方法显着优于国家的最先进的直接和间接的方法在各种现实世界的设置，无论是在跟踪精度和鲁棒性。
Direct Sparse Odometry (DSO) is a visual odometry method based on a novel, highly accurate sparse and direct structure and motion formulation. 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. Since our method does not depend on keypoint detectors or descriptors, it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on essentially featureless 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.