One sensor learning from another

Sensor interpretation in mobile robots often involves an inverse sensor model of the sensors used. Building inverse sensor models for sonar sensor assemblies is a particularly difficult problem that has received much attention in past years. A common solution is to train neural networks using supervised learning. However, large amounts of training data are typically needed, consisting, for example, of scans of recorded sonar data which are labeled with manually constructed teacher maps. Obtaining these training data is an error-prone and time-consuming process. We suggest that it can be avoided, if an additional sensor like a laser scanner is also available which can act as the feeding signal. We show successfully trained inverse sensor models for sonar interpretation using laser scan data.