Approximate robotic mapping from sonar data by modeling perceptions with antonyms
Information Sciences: an International Journal
| Hi-index | 0.00 |
An autonomous mobile robot operating in an unknown indoor environment often needs to map the environment while localizing within the map. Feature-based world models including line and point features are widely used by researchers. This paper presents a novel delayed-classi-fication algorithm to categorize these features using a recently developed high-performance sonar ring within a simultaneous localization and map-building (SLAM) process. The sonar ring sensor accurately measures range and bearing to multiple targets at near real-time repetition rates of 11.5 Hz to 6 m range, and uses 24 simultaneously fired transmitters, 48 receivers and multiple echoes per receiver. The proposed algorithm is based on hypothesis generation and verification using the advanced sonar ring data and an extended Kalman filter (EKF) approach. It is capable of initiating new geometric features and classifying them within a short distance of travel of about 10 cm. For each new sonar reading not matching an existing feature, we initiate a pair of probational line and point features resulting from accurate range and bearing measurements. Later measurements are used to confirm or remove the probational features using EKF validation gates. The odometry error model of the filter allows for variations in effective wheel separation required by pneumatic robot tyres. The implementation of the novel classification and SLAM algorithm is discussed in this paper and experimental results using real sonar data are presented.