Sonar interpretation learned from laser data

Sensor interpretation in mobile robots often involves an inverse sensor model, which generates hypotheses on specific aspects of the robot's environment based on current sensor data. Building inverse sensor models for sonar sensor assemblies is a particularly difficult problem that has received much attention in the past few 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 have successfully trained inverse sensor models for sonar interpretation using laser scan data. In this paper, we describe the procedure we used and the results we obtained.