2차원 레이저 거리계를 이용한 수직/수평 다각평면 기반의 위치인식 및 3차원 지도제작

This study aimed to explore how a three-dimensional (3D) map affects the acquisition of survey knowledge in complex virtual environments (VEs). Subject navigated in virtual and real subway stations, where the virtual station was augmented with either a 3D floor map, a 3D building map, or neither. After subject's navigation in the station, they made the judgment of spatial relative directions between objects. The analyses of response time showed that participants responded faster when they were assisted by the 3D floor or building map than without a map aid in virtual stations. There was no significant difference between survey knowledge acquired in the virtual station augmented with the 3D floor or building map and in the real station. Participants with the 3D floor map performed best in spatial judgments. Implications of these findings to a 3D map design were discussed.

Vehicle self-localization techniques, such as GNSS and Velodyne, can benefit greatly by accessing to an accurate 3D building map, especially in urban areas. Though, an accurate automatic 3D building map reconstruction is challenging in dense urban areas, in which buildings are located close to each other. In general, 3D building map can be generated from adding accurate height information of the building roofs to a detailed 2D map. But 2D building maps usually only provide footprint of the buildings which is not enough for 3D shape modeling of complicated buildings in urban areas such as Shinjuku, Tokyo, Japan. On the other hand, with the development of laser scanning technology, airborne laser scanning (ALS) has now become easily accessible for large scale 3D mapping. However, an automatic 3D map reconstruction using only airborne point cloud is not feasible for dense urban areas, because of the difficulty of defining boundary of each building individually. This paper presents an automatic 3D building map reconstruction technique which integrates airborne laser point cloud with 2D boundary map. Results from Shinjuku area is evaluated for the proposed technique.

SummaryConventional simultaneous localization and mapping (SLAM) has concentrated on two-dimensional (2D) map building. To adapt it to urgent search and rescue (SAR) environments, it is necessary to combine the fast and simple global 2D SLAM and three-dimensional (3D) objects of interest (OOIs) local sub-maps. The main novelty of the present work is a method for 3D OOI reconstruction based on a 2D map, thereby retaining the fast performances of the latter. A theory is established that is adapted to a SAR environment, including the object identification, exploration area coverage (AC), and loop closure detection of revisited spots. Proposed for the first is image optical flow calculation with a 2D/3D fusion method and RGB-D (red, green, blue + depth) transformation based on Joblove–Greenberg mathematics and OpenCV processing. The mathematical theories of optical flow calculation and wavelet transformation are used for the first time to solve the robotic SAR SLAM problem. The present contributions indicate two aspects: (i) mobile robots depend on planar distance estimation to build 2D maps quickly and to provide SAR exploration AC; (ii) 3D OOIs are reconstructed using the proposed innovative methods of RGB-D iterative closest points (RGB-ICPs) and 2D/3D principle of wavelet transformation. Different mobile robots are used to conduct indoor and outdoor SAR SLAM. Both the SLAM and the SAR OOIs detection are implemented by simulations and ground-truth experiments, which provide strong evidence for the proposed 2D/3D reconstruction SAR SLAM approaches adapted to post-disaster environments.

Robust simultaneous localization and map building (SLAM) is a key issue for mobile robot in presence of faults. In the paper, an adaptive evolutionary particle filter is designed to achieve robust SLAM for wheeled mobile robot when the laser range finder is subjected to errors. Firstly, a robust perception model for laser range finder is presented. The robustness of the provided model is two folds, (1) error beams of laser range finder are filtered out with segment analysis method, and (2) beams occluded by dynamical objects are filtered out with a high pass filter. Secondly, an adaptive mutation scheme is adopted to recover the diversity of the particles after resampling stage. Lastly, the presented method is testified in a real mobile robot.

3D map building and path planning serve as two essential tasks for mobile robot to work within a complex outdoor environment. An elevation map built from 3D laser points is utilised to extract terrain feature while ground surface character is acquired from visual information by matching characteristics vectors. By projecting the units of the elevation map into the image, the fusion of terrain feature and ground surface character is achieved using statistical method. Then the units which fuse multi-sensor information are evaluated and given traversable weights to extract constraints for autonomous path planning in outdoor scene. After clustering the units into several regions, a hierarchical path planning strategy uses A* and Probabilistic Roadmap Method (PRM) to plan region paths and unit paths, respectively. The PRM is improved by choosing the units with the largest traversable weights in the regions to ensure a uniform distribution instead of random distribution. Experiment results show the validity and practicability of the proposed approaches.

Simultaneous localization and mapping is a stochastic map building method which permits consistent robot navigation without requiring an a priori map. The map is built incrementally as the robot observes the environment with its on-board sensors and, at the same time, is used to localise the robot. In this paper simultaneous localization and map building is performed with a metric approach which permits both precision and robustness. The most important innovation of the approach is the way how errors in the control of robot localisation are handled by map building using the information of the landmarks localization. The method uses data from a laser scanner to extract distances and orientations of landmark and combines control localization and metric paradigm. The metric approach, based on the Kalman Filter, uses a new concept to avoid the problem of the drift in odometry. The simulation section will validate the maps representation approach and presents different aspect of environments.

Localization and mapping is an important factor for mobile robots. 3D map information is preferred even for mobile robots that move in a 2D space. In order to build 3D map of an unknown environment, we used only one laser scanner, and obtained 3D environmental information by continuously tilting the laser scanner itself using a pulse motor. We applied 2D scan matching method by extracting a horizontal space data of scan information in order to connect scan data for 3D map building. Then, we conducted 3D map building experiments and confirmed that 3D map was obtained in a quite high speed, and the resolution is the same as 2D map.

This thesis presents a recent research on the problem of environmental modeling for both localization and map building for wheel-based, differential driven, fully autonomous and self-contained mobile robots. The robots behave in an indoor office environment. They have a multi-sensor setup where the encoders are used for odometry and two exteroperceptive sensors, a 360° laser scanner and a monocular vision system, are employed to perceive the surrounding. The whole approach is feature based meaning that instead of directly using the raw data from the sensor features are firstly extracted. This allows the filtering of noise from the sensors and permits taking account of the dynamics in the environment. Furthermore, a properly chosen feature extraction has the characteristic of better isolating informative patterns. When describing these features care has to be taken that the uncertainty from the measurements is taken into account. The representation of the environment is crucial for mobile robot navigation. The model defines which perception capabilities are required and also which navigation technique is allowed to be used. The presented environmental model is both metric and topological. By coherently combining the two paradigms the advantages of both methods are added in order to face the drawbacks of a single approach. The capabilities of the hybrid approach are exploited to model an indoor office environment where metric information is used locally in structures (rooms, offices), which are naturally defined by the environment itself while the topology of the whole environment is resumed separately thus avoiding the need of global metric consistency. The hybrid model permits the use of two different and complementary approaches for localization, map building and planning. This combination permits the grouping of all the characteristics which enables the following goals to be met: Precision, robustness and practicability. Metric approaches are, per definition, precise. The use of an Extended Kalman Filter (EKF) permits to have a precision which is just bounded by the quality of the sensor data. Topological approaches can easily handle large environments because they do not heavily rely on dead reckoning. Global consistency can, therefore, be maintained for large environments. Consistent mapping, which handle large environments, is achieved by choosing a topological localization approach, based on a Partially Observable Markov Decision Process (POMDP), which is extended to simultaneous localization and map building. The theory can be mathematically proven by making some assumptions. However, as stated during the whole work, at the end the robot itself has to show how good the theory is when used in the real world. For this extensive experimentation for a total of more than 9 km is performed with fully autonomous self-contained robots. These experiments are then carefully analyzed. With the metric approach precision with error bounds of about 1 cm and less than 1 degree is further confirmed by ground truth measurements with a mean error of less than 1 cm. The topological approach is successfully tested by simultaneous localization and map building where the automatically created maps turned out to work better than the a priori maps. Relocation and closing the loop are also successfully tested.

Urban underground integrated pipe corridor is used for the water, electricity, gas, communications, and other pipelines into the underground space to effectively alleviate road traffic congestion, urban construction infrastructure, and other problems. After the completion of the corridor, the corridor inspection is a huge problem, as the underground space is complex and difficult to locate. For the need to detect the concentration of toxic and harmful gases inside the corridor, it is difficult to ensure the safety of the lives of the underground staff. With the development of artificial intelligence and digital twin, the corridor inspection robot gradually becomes the “perfect candidate” for inspection work. Thus, we present a system design of 3D LIDAR-based robot positioning and map building underground where GPS is not available.

This paper represents a SLAM(simultaneous localization and map building) system with odometry and indoor GPS sensor. This paper presents an accurate localization method by relative and absolute sensor fusion for map building system of mobile robot, we developed a sensor based absolution position tracking algorithm and grid based map building algorithm using this algorithm. A wall following decision engine like a fuzzy algorithm was proposed for map building navigation. Generally, while the robot is navigating the environment for map building position is estimated by odometry. However the position estimation using encoder data is not accurate owing to slipping and odometry linearization error. To solve this problem we proposed a sensor fusion method using encoder and indoor GPS. We proved this system' s validity through field test.

A new three-dimensional (3D) map building method based on Laser Range Finder (LRF) has been proposed in this research, performing a surface estimation with the Iterative Closest Point (ICP) algorithm. While a mobile robot is navigating in an unknown environment, the entire environment cannot be scanned by LRF since kinematic features of the mobile robot and surface conditions are dynamically changing. To resolve this difficulty in building a 3D map while the mobile robot is navigating, a surface estimation ICP algorithm is proposed, which is based on the continuity of the environment around mobile robot. That is, this new algorithm recovers the un-scanned area by estimating feature points in the neighboring two regions based on the continuous environment information. The effectiveness of proposed algorithm has been demonstrated through real experiments of the mobile robot navigation.

This paper proposes a robust 3D map-building system for a mobile robot using a LRF (Laser Range Finder). The LRF can be utilized and installed on the mobile robot for the map building and the autonomous navigation. The scanning operations are performed while the mobile robot is moving on the ground without stops. The effectiveness of the proposed map building scheme has been demonstrated through the real navigation experiments in the outdoor environment.

This paper develops a simultaneous localisation and map building (SLAM) algorithm which utilises the split covariance intersection (SCI) update rule. This algorithm decomposes estimates into a correlated component (whose precise structure is unknown) and a lower bound on an independent component. For a map of n beacons, the storage is O(n) and the computational costs are constant irrespective of map size. In a simple simulation example we show that the SCI algorithm, through exploiting a lower bound on independent information, performs substantially better than the traditional covariance intersection SLAM algorithm.

This paper describes recent progress toward the development and deployment of large-scale simultaneous localisation and map building (SLAM) systems. Two main theoretical results are presented. The first describes a closed form solution to the SLAM problem. This solution explains the many experimental and theoretical results obtained so far in the study of the SLAM problem and allows a precise means of analysing the performance of large scale, efficient, SLAM algorithms. The second result describes how best to choose landmarks to best maintain the performance of a large scale SLAM systems, and consequently which sets of landmarks may be deleted from a SLAM algorithm without compromising overall performance. This is the key to large-scale SLAM problems as it provides an efficient and systematic means of reducing the computational complexity of the problem with minimal loss of optimality. We conclude by describing an experimental program aimed at verifying and testing these SLAM algorithms in particular applications.

This paper presents the development of the JKU-ITS Last Mile Delivery Robot. The proposed approach utilizes a combination of one 3D LIDAR, RGB-D camera, IMU and GPS sensor on top of a mobile robot slope mower. An embedded computer, running ROS1, is utilized to process the sensor data streams to enable 2D and 3D Simultaneous Localization and Mapping, 2D localization and object detection using a convolutional neural network.KeywordsLast mile deliveryMobile robotSensorsSensor-fusionROS1