An advanced sonar morphology-based posterior approach (SMP) to building an occupancy grid map for a mobile robot is proposed in this study. It is very important for a mobile robot to find its surrounding saliencies and to localize itself for indoor navigation. Ultrasonic sensors are of great practical value in building environmental maps and for autonomous operation. However, grid maps constructed by ultrasonic sensors cannot typically form a realistic representation of a given environment due to incorrect sonar measurements caused by specular reflection and wide beam width. The sonar sensor model proposed in this study, in which the negative effect of incorrect sonar measurements is minimized by geometric association with sonar footprints, is adopted to build a high-quality grid map. Experimental results and evaluations in home and corridor environments demonstrate the validity of the proposed methods.

An algorithm for three-level hierarchical sensor fusions has beenproposed and applied to environment map building with enhanced accuracy and efficiency. The algorithm was realized through the two new types of sensor modules, which are composed of a halogen lamp-based active vision sensor and a semicircular ultrasonic (US) and infrared (IR) sensor system. In the first-level fusion, the US and IR sensor information is utilized in terms of the geometric characteristics of the sensor location. In the second-level fusion, the outputs from the US and IR sensors are combined with the sheet of halogen light through a proposed rule base. In the third-level fusion, local maps from the first- and second-level fusion are updated in a probabilistic way for a very accurate environment local map. A practical implementation has been carried out to demonstrate the efficiency and accuracy of the proposed hierarchical sensor fusion algorithm in environment map building.