Performance Comparison of Visual Teach and Repeat Systems for Mobile Robots.

In practical work scenarios, it is often necessary to repeat specific tasks, which include navigating along a desired path. Visual teach and repeat systems are a type of autonomous navigation in which a robot repeats a previously taught path using a camera and dead reckoning. There have been many different teach and repeat methods proposed in the literature, but only a few are open-source. In this paper, we compare four recently published open-source methods and a Boston Dynamics proprietary solution embedded in a Spot robot. The intended use for each method is different, which has an impact on their strengths and weaknesses. When deciding which method to use, factors such as the environment and desired precision and speed should be taken into consideration. For example, in controlled artificial environments, which do not change significantly, navigation precision and speed are more important than robustness to environment variations. However, the appearance of unstructured natural environments varies over time, making robustness to changes a crucial property for outdoor navigation systems. This paper compares the speed, precision, reliability, robustness, and practicality of the available teach and repeat methods. We will outline their flaws and strengths, helping to choose the most suitable method for a particular utilization.