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This paper presents a new method of localization and map building of mobile robot based on mixed map model using LRF (Laser Range Finder). The mixed model composed of occupancy grids and line character maps is utilized to represent the environment map. Firstly, the LRF models and Bayes rules are used to construct a local occupancy grid map. Then, we extract obstacles points to get a precise geometry...
In continuation of our previous work on visual, appearance-based localization in manually built maps in this paper we present a novel appearance-based, visual SLAM approach. The essential contribution of this work is, an adaptive sensor model which is estimated online and a graph matching scheme to evaluate the likelihood of a given topological map. Both methods enable the combination of an appearance-based,...
Intelligent vehicles and autonomous robots are viable in complex environments, the reliable and robust localization function of which is necessary. Due to the large variability and uncertainty of complex environments, it is difficult to rely on a specific method or a set of sensor data to correctly and robustly estimate the robot pose. The key to solving the localization problem is to optimally use...
Simultaneous localization and mapping (SLAM) has emerged as a key capability for autonomous mobile robots navigating in unknown environments. The basic idea behind SLAM is to concurrently obtain a map of the environment and an estimate of where the robot is placed within this map. In other words, the map and the robot's pose have to be estimated at the same time, given the same data set. This paper...
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