Parameterized Sensor Model and Handling Specular Reflections for Robot Map Building

Map building is one of the most popular classical problems in mobile robotics field and sensor model is the way to interpret raw sensory information in spatial world especially for building map. For mobile robots, working in indoor environment, Ultrasonic Sonar(US) is famous because of its availability, wide angle and long range sensing ability etc, in spite of some problems. Among them, Specular Reflection problem is the most prominent one. A parameterized sensor model for map building by using occupancy grid with Bayesian updating has been proposed as the first part of this MS Thesis project. To optimize the parameter of the proposed model, a measurement of map goodness strategy has been applied. One new approach, without handcrafted map of the actual environment, for measuring the goodness of maps has been tested with implementation result. Three different processes, statistical analysis, map scoring technique over binary maps and derivative of image using 8-connecting neighbor concept borrowed from image processing field, have been studied to find the measurement of map goodness. Though satisfactory result has not been achieved for measuring goodness of maps without ground map, proposed sensor model generates better maps than standard sensor model, described in R. Murphy [27], with same parameter set has been proved by this study. Dealing with specular reflection problem while using ultrasonic sonar has been investigated in the second part of this MS Thesis project. The robot has a number of ultrasonic sonar that partly overlap, especially over time when the robot is moving. Based on this redundancy, one data processing algorithm, HSR (Handling Specular Reflection) using linear feature which is called Estimated Wall in this project, has been developed. HSR is off line data processing in batch mode. The HSR algorithm is shown to improve the quality of maps in two different experimental environments.