Abstract
High-precision measurement is a common task in many engineering applications. In these cases, sensor fusion algorithms represented by Kalman filter and its variants are effective and practical. It is introduced in the article, how to use this sensor fusion algorithm in a high-precision tracking task, where only one sensor (tacheometer) is used in the project. It is also discussed in the article how to build estimation models, including measurement model and motion model, for specific robot positioning problems. A variety of related models are compared in this article. The best model that utilize the prior knowledge of robot motion is proposed, which can not only meet the high precision requirements, but also have robustness to the robot operating environment.
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