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Reducing drift in differential tracking

机译：减少差分跟踪中的漂移

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摘要

We present methods for turning pair-wise registration algorithms into drift-free trackers. Such registration algorithms are abundant, but the simplest techniques for building trackers on top of them exhibit either limited tracking range or drift. Our algorithms maintain the poses associated with a number of key frames, building a view-based appearance model that is used for tracking and refined during tracking. The first method we propose is batch oriented and is ideal for offline tracking. The second is suited for recovering egomotion in large environments where the trajectory of the camera rarely inter sects itself, and in other situations where many views are necessary to capture the appearance of the scene. The third method is suitable for situations where a few views are sufficient to capture the appearance of the scene, such as object-tracking. We demonstrate the techniques on egomotion and head-tracking examples and show that they can track for an indefinite amount of time without accumulating drift.
机译：我们提出了将成对注册算法转变为无漂移跟踪器的方法。这样的配准算法非常丰富，但是在其之上构建跟踪器的最简单技术却显示出有限的跟踪范围或漂移。我们的算法维持与多个关键帧相关的姿势，构建了一个基于视图的外观模型，该模型用于跟踪并在跟踪期间进行完善。我们提出的第一种方法是面向批处理的，非常适合脱机跟踪。第二种适用于在相机的轨迹很少与自身相交的大型环境中以及在需要许多视图才能捕获场景外观的其他环境中恢复自我运动。第三种方法适用于一些视图足以捕获场景外观的情况，例如对象跟踪。我们演示了自我运动和头部跟踪示例中的技术，并表明它们可以无限期地跟踪而不会累积漂移。

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