| Summary: | 碩士 === 國立清華大學 === 電機工程學系 === 98 === Simultaneous Localization and Mapping (SLAM) is an important technique for robotic system navigation. Due to the high complexity of the algorithm, SLAM usually needs long computational time or large amount of memory to achieve accurate results. In this paper, we present a lightweight Rao-Blackwellized Particle Filter (RBPF) based SLAM algorithm for indoor environments, which uses line segments extracted from the laser range finder as the fundamental map structure so as to reduce the memory usage. Since most major structures of indoor environments are usually orthogonal to each other, we can also efficiently increase the accuracy and reduce the complexity of our algorithm by exploiting this orthogonal property of line segments; that is, we treat line segments that are parallel or perpendicular to each other in a special way when calculating the importance weight of each particle. In our work, the orthogonal scan lines extracted from the sensor can be identified by a reference direction, which is modified each time the local map is built. By dynamically modifying the reference direction, our algorithm can successfully detect the orthogonal lines even when the robots initial pose is not aligned with the major structures of the environment.
Experimental Results shows that our work not only is capable of drawing maps in complex indoor environments but also can accurately closes a loop after the robot has traveled a long distance. Results also shows that our proposed light-and fast SLAM only needs very low amount of memory and much less computational time as compared to other grid map based RBPF SLAM algorithms.
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