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finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robot localization and mapping algorithm, which uses scale -invariant image features as natural landmarks ...
to mobile robot map -building that KEY WORDS— mapping , localization , autonomous agents, handles qualitatively different types of uncertainty by combining the cognitive robotics strengths of topological ...
of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi- scale adaptive sampling strategy. The proposed parametric sampling ...
, such that the robot drives at or near its capabilities while respecting path and localization constraints. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling ...
method is used to quickly search the high-precision pose of the robot . In the real-time tracking component, small- scale correlation sampling is performed on the high-resolution environment grid map ...
simultaneous localization and mapping algorithm called MIM_SLAM is proposed in this paper. This algorithm is applied in mobile robots to build a map on a non-flat road with a 3D LiDAR sensor. MIM_SLAM’s main ...
The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping ...
fuse measurements from robot ‐mounted sensors and pervasive network devices.The navigation system of a mobile robot consists of various technologies such as localization , mapping , recognition, perception ...
observation and local map and matching between different local maps are carried out. The uncertainty of the results-matching is described by the Fisher information matrix. Secondly, the robot pose is optimized ...
and heavy snow, rain and drizzle. First, we describe the system, which relies on point cloud registration to localize a mobile robot through a boreal forest, while simultaneously building a map . We ...
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