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With the rapid development of robotics and in-depth research of automatic navigation technology, mobile robots have been applied in a variety of fields. Map construction is one of the core research focuses of mobile robot development. In this paper, we propose an autonomous map calibration method using visible light positioning (VLP) landmarks and Simultaneous Localization and Mapping (SLAM). A layout map of the environment to be perceived is calibrated by a robot tracking at least two landmarks mounted in the venue. At the same time, the robot's position on the occupancy grid map generated by SLAM is recorded. The two sequences of positions are synchronized by their time stamps and the occupancy grid map is saved as a sensor map. A map transformation method is then performed to align the orientation of the two maps and to calibrate the scale of the layout map to agree with that of the sensor map. After the calibration, the semantic information on the layout map remains and the accuracy is improved. Experiments are performed in the robot operating system (ROS) to verify the proposed map calibration method. We evaluate the performance on two layout maps: one with high accuracy and the other with rough accuracy of the structures and scale. The results show that the navigation accuracy is improved by 24.6 cm on the high-accuracy map and 22.6 cm on the rough-accuracy map, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of robotics and in-depth research of automatic navigation technology, mobile robots have been applied in a variety of fields. Map construction is one of the core research focuses of mobile robot development. In this paper, we propose an autonomous map calibration method using visible light positioning (VLP) landmarks and Simultaneous Localization and Mapping (SLAM). A layout map of the environment to be perceived is calibrated by a robot tracking at least two landmarks mounted in the venue. At the same time, the robot’s position on the occupancy grid map generated by SLAM is recorded. The two sequences of positions are synchronized by their time stamps and the occupancy grid map is saved as a sensor map. A map transformation method is then performed to align the orientation of the two maps and to calibrate the scale of the layout map to agree with that of the sensor map. After the calibration, the semantic information on the layout map remains and the accuracy is improved. Experiments are performed in the robot operating system (ROS) to verify the proposed map calibration method. We evaluate the performance on two layout maps: one with high accuracy and the other with rough accuracy of the structures and scale. The results show that the navigation accuracy is improved by 24.6 cm on the high-accuracy map and 22.6 cm on the rough-accuracy map, respectively.

This work considers the path planning problem of personal mobility vehicle (PMV) for indoor navigation using the Artificial Potential Field (APF) method. The APF method sometimes suffers from an infinite loop problem during the planning phase when the goal is blocked by obstacles with certain characteristics. To address the issue, this study deploys the map augmentation method for replanning. When infinite loop situations occur, the map is transformed and the search for drivable path is initiated. The method successfully generates a feasible trajectory when the map is rotated at a certain angle. The scenario of successful planning is shown in the result. [ABSTRACT FROM AUTHOR]

This work considers the path planning problem of personal mobility vehicle (PMV) for indoor navigation using the Artificial Potential Field (APF) method. The APF method sometimes suffers from an infinite loop problem during the planning phase when the goal is blocked by obstacles with certain characteristics. To address the issue, this study deploys the map augmentation method for replanning. When infinite loop situations occur, the map is transformed and the search for drivable path is initiated. The method successfully generates a feasible trajectory when the map is rotated at a certain angle. The scenario of successful planning is shown in the result.

This paper addresses the weighted orthogonal Procrustes problem of matching stochastically perturbed point clouds, formulated as an optimization problem with a closed-form solution. A novel uncertainty characterization of the solution of this problem is proposed resorting to perturbation theory concepts, which admits arbitrary transformations between point clouds and individual covariance and cross-covariance matrices for the points of each cloud. The method is thoroughly validated through extensive Monte Carlo simulations, and particularly interesting cases where nonlinearities may arise are further analyzed. [ABSTRACT FROM AUTHOR]

Highlights: [•] Maps provide a suitable cartographic base for historical analyses. [•] The main problem is the heterogeneity of the quality of historical data. [•] The quality of historical maps must be analyzed prior to the application of their information. [•] The evolutionary algorithms NSGA-II and msPESA are assessed in this study. [•] Solutions obtained by NSGA-II dominate solutions obtained by msPESA. [Copyright &y& Elsevier]

Abstract: Adaptive mapping and real-time spatial data delivery are currently major research topics in the fields of Web-GIS and Location Based Services (LBS). In order to successfully implement these paradigms a methodology to progressively adopt or transform an arbitrary map representation in an arbitrary fashion on-the-fly is necessary. Such a methodology was previously considered not feasible due to the high computational complexity associated with existing automated map generalisation methodologies. This paper presents a methodology, inspired by related research in the field of computer graphics, which offers the potential to reduce such associated computational complexity. This is achieved using an approach to map transformation which takes advantage of the geometric coherence between a user''s spatial data requirements. [Copyright &y& Elsevier]

This article deals with methods of navigation and mapping of mobile robots in an indoor environment, for example, laboratories, building corridors, and so on. It explains the proposed solution of global navigation in more detail through the application of potential field method and its transformation into the topological map. Two separate software tools were designed for simulation of wheeled mobile robot behavior in the authors’ workplace. The first software uses the metric form of space representation and it can simulate tactic level of global navigation, while the second one deals with its transformation into the topological map and can be used for strategic level of global navigation. This is how we can get the so-called multilayer map system suitable for different tasks of mobile robot navigation and path planning. [ABSTRACT FROM AUTHOR]