1A1-F10 移動ロボットに搭載したレーザスキャナによる3次元地図の生成

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.

3D mapping of outdoor environments by scan matching and motion averaging

Three-dimensional (3D) mapping deals with the problem of building a map of the unknown environments explored by a mobile robot. In contrast to 2D maps, 3D maps contain richer information of the visited places. Besides enabling robot navigation in 3D, a 3D map of the robot surroundings could be of great importance for higher-level robotic tasks, like scene interpretation and object interaction or manipulation, as well as for visualization purposes in general, which are required in surveillance, urban search and rescue, surveying, and others. Hence, the goal of this thesis is to develop a system which is capable of reconstructing the surrounding environment of a mobile robot as a three-dimensional map. Microsoft Kinect camera is a novel sensing sensor that captures dense depth images along with RGB images at high frame rate. Recently, it has dominated the stage of 3D robotic sensing, as it is low-cost, low-power. For this work, it is used as the exteroceptive sensor and obtains 3D point clouds of the surrounding environment. Meanwhile, the wheel odometry of the robot is used to initialize the search for correspondences between different observations. As a single 3D point cloud generated by the Microsoft Kinect sensor is composed of many tens of thousands of data points, it is necessary to compress the raw data to process them efficiently. The method chosen in this work is to use a feature-based representation which simplifies the 3D mapping procedure. The chosen features are planar surfaces and orthogonal corners, which is based on the fact that indoor environments are designed such that walls, ground floors, pillars, and other major parts of the building structures can be modeled as planar surface patches, which are parallel or perpendicular to each other. While orthogonal corners are presented as higher features which are more distinguishable in indoor environment. In this thesis, the main idea is to obtain spatial constraints between pairwise frames by building correspondences between the extracted vertical plane features and corner features. A plane matching algorithm is presented that maximizes the similarity metric between a pair of planes within a search space to determine correspondences between planes. The corner matching result is based on the plane matching results. The estimated spatial constraints form the edges of a pose graph, referred to as graph-based SLAM front-end. In order to build a map, however, a robot must be able to recognize places that it has previously visited. Limitations in sensor processing problem, coupled with environmental ambiguity, make this difficult. In this thesis, we describe a loop closure detection algorithm by compressing point clouds into viewpoint feature histograms, inspired by their strong recognition ability. The estimated roto-translation between detected loop frames is added to the graph representing this newly discovered constraint. Due to the estimation errors, the estimated edges form a non-globally consistent trajectory. With the aid of a linear pose graph optimizing algorithm, the most likely configuration of the robot poses can be estimated given the edges of the graph, referred to as SLAM back-end. Finally, the 3D map is retrieved by attaching each acquired point cloud to the corresponding pose estimate. The approach is validated through different experiments with a mobile robot in an indoor environment

An indoor service robot can establish a 3D map of the environment by a visual sensor. When the robot uses the established map again, there is a situation that the robot's view is in a new position and the relative position of the map can not be obtained. A relocalization algorithm based on multi-dimensional map information is proposed to solve the problem. Based on the established 3D environment map, the algorithm samples a 2D map and matches the map with the 2D observation information of the current frame based on the particle filter algorithm to obtain the robot's initial position. Then, the target point cloud corresponding to the current frame is segmented in the 3D map, and the exact pose relationship between the current frame cloud and the target point cloud is calculated by the iterative closest point algorithm. The SVD decomposition and g2o optimization are used in the method to improve the computational efficiency and accuracy. Finally, the relocalization experiments under the wide range of robot perspective are carried out in the indoor environment. The results verify the effectiveness and robustness of the algorithm.

Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator.

While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.

A new optoelectronic system based on a projection unit in which light, coming from a laser diode coupled to an optic fiber impinges on a diffractive optical element (DOE) to produce sinusoidal fringes is proposed for three-dimensional (3-D) texture measurement. If the projected fringe pattern is viewed at an angle different from the projection angle, the fringe profile is phase-modulated by the 3-D object shape. The 3-D map information is obtained with the aid of a fringe analyzer based on phase-shifting synthetic moiré pattern, Fast Fourier Transform (FFT), signal demodulation techniques and a robust and fast phase unwrapping performed by a specially developed software. The proposed system is based on a simple and low cost equipment; furthermore, it is suitable for in situ measurements also by nonskilled operators. Some experimental examples illustrate its performance.

With the development of the tourism sphere, its information support is also developing, in particular, represented with cartographic works. One of the forms of cartographic support for tourism is three-dimensional artistic maps (3D-maps, panoramic maps) and cartographic web applications. By our understanding the panoramic map of the city is a cartographic image of the urban environment, made with regard to linear and aerial perspectives on the principle of artistic painting and created an illusory sense of volumetricity. Such maps illustrated the historical parts of cities, park zones, museums-estates, individual architectural monuments become popular souvenirs, but their handwritten creation is rather labor-intensive and requires high artistic skills. The research proposes the author’s methodology and technology for creating and updating of three-dimensional artistic maps mainly of small and medium cities and towns using modern information technology, which involves a rational combination of works done manually (handwritten) and with the usage of graphic editors (automated). The preferred cartographic projections for mapping of different territories have been determined (external perspective projection with positive image, taking into account the curvature of the globe surface – for vast territories, central projection – for small territories, including individual monuments). The most optimal conditions for three-dimensional illusory representation of cities have also been established (the constant height of the observation point, the projection with parallel rays at an angle of 60° to the picture plane, the display only the front and middle parts of the map). Special attention is paid to the usage of automated libraries of signs, textures and colors, developed by the author in digital form and containing individual constructive elements of urban environment objects, which greatly simplifies the process of panoramic map creating. The proposed combined handwritten-automated methodology involves several stages: preparatory, field and office. At the last stage, the visualization of objects is carried out, including the construction of the plan of the mapped territory, transforming it into the perspective projection, the construction of building frames and filling them with structural elements, color design and drawing detailing. This methodology is also used for updating of artistic maps (actualization of their content).

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.

In this paper, a graph-based hierarchical SLAM framework is proposed which ensures not only the high-speed operation of graph-based SLAM, but also feasibility of large-scale 3D map building. Both local and global level graph-based SLAM will be operated. They can be concurrently implemented in different computing units but must maintained communication which is called as a session. During each session, local level SLAM will create a pose-graph containing trajectory for mobile robot and information for local maps. To avoid communication congestion, raw density point cloud of each pose-node will be reduced into a sparser one by voxel grid filtering. When a session is closed, duo-graph strategy is executed to guarantee the consistency between successive local map. To associate massive local map information and closing loops in large-scale environment, graph-based algorithm will also be implemented in global level SLAM.Two experiments are carried out in the real indoor environment. In the first experiment, SLAM process is greatly accelerated in this framework by distributing the whole SLAM task into different level SLAM entities: local level SLAM operated on robot and global on laptop. It shows that this framework is scalable and it can be implemented in CS(Client-Server) model. Second experiment is conducted in our biggest work office around which we control the robot traverse for three times. It demonstrates that this framework can simultaneously keep fast graph-based SLAM in local-end and generate consistent and convergent 3D map in global-end SLAM process.KeywordsPoint CloudMobile RobotSuper NodeGraph OptimizationCurrent SessionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper presents an automatic guidance of an autonomous land vehicle based on DGPS and a 3D map. To compensate the positioning error of GPS, we used environmental sensing systems including video cameras, ultrasonic sensors and a 3D scanning laser rangefinder. The 3D map provides 3D environmental information and indicates landmarks for an image processing system to correct the positioning error of GPS. The image processing system also plays the role of obstacle detection. The 3D scanning laser rangefinder is used for speedy and accurate detection of obstacles. This paper describes the DGPS and a 3D map navigated driving system and presents the experimental results.

Personalizing workspace environments for applications involving 3D mapping has become an essential requirement across diverse settings. However, constructing such maps for customized use cases as in the scope remains a nontrivial task, often requiring a high-reliability hardware as a baseline and involving considerable implementation complexity and cost. In contrast, frontier-based exploration methods operating in 2D space have demonstrated high reliability and completeness in map construction, suggesting strong potential for extension to autonomous 3D mapping. Nonetheless, the primary risk in applying this approach directly to 3D space is the expansion of the exploration tree into regions that are physically unreachable or prone to collision due to hardware limitations in 3D sensing. To address this, our research employs a minimal hardware setup consisting of a 2D LiDAR and a depth camera mounted on an indoor autonomous ground vehicle (AGV). Sensor data from these heterogeneous sources is fused to provide more reliable and accurate environmental perception. Based on this setup, the frontier-based exploration algorithm is applied within the 2D spatial plane, while 3D obstacle information is concurrently integrated to prevent the robot from expanding frontiers into regions that are either height-constrained or provide minimal informational gain. Both quantitative results in simulation and qualitative evaluations in real-world scenarios show that the proposed system achieves two major improvements: sensor fusion enables more accurate and stable localization, and incorporating 3D obstacle information improves the efficiency of frontier expansion by reducing unnecessary map exploration and helping the robot avoid local minima. These findings indicate that the proposed approach holds strong promise for autonomous indoor 3D mapping applications.

The paper deals with the three-dimensional (3D) modelling based on data acquired from 2D laser sensor and IMU (Inertial Measurement Unit) attached to the UAV (Unmanned Aerial Vehicle). The used multi-sensor unit produces 2D scans and provides information about Euler angles or quaternions. These angles are used to describe the UAV orientation in 3D space, more precisely the orientation of the laser sensor. In order to generate 3D occupancy map it is necessary to rotate the laser sensor around its axis yielding the 2D scans being mapped on 3D space using quaternions. Inertial sensor and Sweep LiDAR laser measurements are transmitted to the single board computer Odroid XU4 (SBC). The data fusion was performed under ROS (Robot Operating System) installed on the SBC, producing 3D space points. These points are transmitted over the network to the central computer on which the UAV localization and mapping processes are done within the ROS. The effectiveness of the proposed system for 3D modelling of the UAV environment is verified by experiment.

Traditional approaches to simultaneous localization and mapping (SLAM) are unable to extract semantic information from the scene or meet the high-level task requirements of robots, and the efficiency of 3D map construction is low. To solve this problem, a 3D semantic map construction system is proposed in the paper to build a 3D semantic map. Firstly, the current position of the camera is estimated and optimized based on ORB-SLAM algorithm, and the globally consistent trajectory and pose are obtained. Then the semantic segmentation network is designed to predict semantic category of every pixel. The 3D semantic point cloud information is generated by combing the semantic information and the object point cloud. The global consistent camera pose estimated by visual SLAM algorithm is integrated with the semantic point cloud information to generate a 3D semantic map. Finally, we use an Octomap which is applied to navigation projects for map storage to reduce the storage capacity of the map. The experimental result verifies the accuracy and efficiency of this method.