Solving the problem of simultaneous location and map construction (slam) is the core of robot autonomous navigation. At present, most algorithms usually only consider the odometer information of mobile robot, therefor...
详细信息
ISBN:
(纸本)9781538612446
Solving the problem of simultaneous location and map construction (slam) is the core of robot autonomous navigation. At present, most algorithms usually only consider the odometer information of mobile robot, therefor, there exists some problems of increasing computation amount caused by a large number of sampling particles and the complexity. So, this paper developed a scheme of map construction and positioning based on rbpf-slam algorithm. The hardware and software platform is set up on the ROS, and it merge the odometer information of the robot into the distance information collected by the laser sensor, which effectively reduces the number of required particles and the uncertainty of the robot's pose estimates in filter prediction phase. Through the experimental, get the conclusion: simultaneous positioning based on RBIT-slamalgorithm and map construction system can create high-precision online raster map in real time, and it is more consistent with the actual map.
Solving the problem of simultaneous location and map construction（slam） is the core of robot autonomous navigation. At present, most algorithms usually only consider the odometer information of mobile robot, therefo...
详细信息
Solving the problem of simultaneous location and map construction（slam） is the core of robot autonomous navigation. At present, most algorithms usually only consider the odometer information of mobile robot, therefor, there exists some problems of increasing computation amount caused by a large number of sampling particles and the complexity. So, this paper developed a scheme of map construction and positioning based on rbpf-slam algorithm. The hardware and software platform is set up on the ROS, and it merge the odometer information of the robot into the distance information collected by the laser sensor, which effectively reduces the number of required particles and the uncertainty of the robot’s pose estimates in filter prediction phase. Through the experimental, get the conclusion: simultaneous positioning based on rbpf-slam algorithm and map construction system can create high-precision online raster map in real time, and it is more consistent with the actual map.
Aiming at the problem about disaster relief under the situation of unknown environment and unknown target orientation, a set of rules for mobile robot autonomous search and map navigation is formulated. Firstly, the m...
详细信息
ISBN:
(纸本)9789881563972
Aiming at the problem about disaster relief under the situation of unknown environment and unknown target orientation, a set of rules for mobile robot autonomous search and map navigation is formulated. Firstly, the mobile robot attempts to read the map of the environment. If it fails, the robot searches for the target according to the wall-touching algorithm in the unknown environment and utilizes the improved rbpf-slam algorithm to implement the map computation. When the map is successfully read, the robot moves along the optimal path planned by A* algorithm. Meanwhile, dynamic window approach is used for dynamic local path planning for the unknown environment which changes indefinitely. Finally, the simulation experiment is carried out to verify the practicability and validity of the set of rules.
Aiming at the problem about disaster relief under the situation of unknown environment and unknown target orientation, a set of rules for mobile robot autonomous search and map navigation is formulated. Firstly, the m...
详细信息
Aiming at the problem about disaster relief under the situation of unknown environment and unknown target orientation, a set of rules for mobile robot autonomous search and map navigation is formulated. Firstly, the mobile robot attempts to read the map of the environment. If it fails, the robot searches for the target according to the wall-touching algorithm in the unknown environment and utilizes the improved rbpf-slam algorithm to implement the map computation. When the map is successfully read, the robot moves along the optimal path planned by A algorithm. Meanwhile, dynamic window approach is used for dynamic local path planning for the unknown environment which changes indefinitely. Finally, the simulation experiment is carried out to verify the practicability and validity of the set of rules.
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