Ying-Leung Ip

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Simultaneous Localization and Map building (SLAM) is referred to as the ability of an Autonomous Mobile Robot (AMR) to incrementally extract the surrounding features for estimating its pose in an unknown location and unknown environment. In this paper, we propose a new technique for extraction of significant map features from standard Polaroid sonar sensors(More)
Map building is one of the core competencies of truly autonomous robots. Numerous techniques have been developed to represent the static and dynamic environments as well as the perceptional sensing frameworks so far. In this paper, on the basis of our previous work, we compare various sensor systems in building the static and dynamic environment map with(More)
This paper presents a segment detection and grouping scheme that allows incremental and on-line learning of indoor environment maps by mobile robots. In this study, the modeling is reEned by first dividing the world into discrete regions as local models. The line segments in local models are extracted by clustering algorithm. The local models are grouped(More)
To make a robot to work for and with human, the ability to simultaneously localize itself, accurately map its surroundings, and safely detect and track moving objects around it is a key prerequisite for a truly autonomous robot. In this paper, we explore the theoretical framework of this problem, i.e. simultaneous localization and mapping (SLAM) with(More)
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