Local Maps Are All You Need: A Review of Topometric Teach and Repeat Navigation

Teach and repeat (T&R) navigation has gained popularity over the past 15 years for its reliable path tracking in global navigation satellite system (GNSS)–denied environments. By using topometric navigation, it blends the scalability of graphs to connect distant places of interest, with metric estimates of a robot's state that allow control algorithms to correct path-tracking errors. Robots have been field-tested in off-road environments and found commercial applications in mining, cleaning, and agriculture. We present a summary of the different interpretations of T&R, introduce the relative strengths of various sensors in T&R, highlight methods that boost the reliability of T&R on robot hardware, and propose that the effectiveness of local maps in topometric navigation comes from a sensor-dependent bias–variance trade-off.