Map Building and SLAM Algorithms
نویسندگان
چکیده
9.
منابع مشابه
New Adaptive UKF Algorithm to Improve the Accuracy of SLAM
SLAM (Simultaneous Localization and Mapping) is a fundamental problem when an autonomous mobile robot explores an unknown environment by constructing/updating the environment map and localizing itself in this built map. The all-important problem of SLAM is revisited in this paper and a solution based on Adaptive Unscented Kalman Filter (AUKF) is presented. We will explain the detailed algorithm...
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Achieving autonomy for robots involves building algorithms for the robots to localize themselves as well as map the environment around them. This problem, called Simultaneous Localization and Mapping (SLAM), has received significant attention in robotics literature. Traditionally, SLAM algorithms represent maps as gigantic matrices (occupancy grids) [3]. They also make use of expensive and ener...
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Simultaneous localization and map-building (SLAM) continues to draw considerable attention in the robotics community due to the advantages it can offer in building autonomous robots. It examines the ability of an autonomous robot starting in an unknown environment to incrementally build an environment map and simultaneously localize itself within this map. Recent advances in computer vision hav...
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The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in [1]–[3], this pa...
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Simultaneous Localisation and Mapping (SLAM) is a popular map building approach in autonomous mobile robotics. Because users demand faster and more effective algorithms, SLAM remains an active area of research. However, the increasing complexity of applications, such as the environments the algorithm is applied to, makes it difficult to debug, evaluate and optimise such algorithms. Our prelimin...
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