Learning State-Dependent Sensor Measurement Models with Limited Sensor Measurements

Abstract

We present a two-stage transfer learning method for training state-dependent sensor measurement models (SDSMMs) with limited sensor data. This method can alleviate collecting sizeable sensor and ground truth data to learn accurate sensor models, especially when we must learn many sensor models (for example, a fleet of autonomous cars, drones, or warehouse robots). In the first stage, we use prior knowledge of the sensor (such as a physical model) to generate a sizeable artificial dataset. Then the artificial dataset is used to pre-train an SDSMM. The second stage fine-tunes the pre-trained SDSMM using a "small" number of data collected by our target real sensor. To our knowledge, we are the first to learn measurement distributions using data generated from a physical model and data from a real sensor. We evaluated our proposed method using the Extended Kalman Particle Filter and a real-world localization dataset collected by several robots. Compared to the prior method, the proposed method achieved comparable performance with as little as ~19% of the real training data.