3D Map Generation Research Articles

Quantitative spectral micro-CT of a CA4+ loaded osteochondral sample with a tabletop system

Micro-computed tomography (μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT) is the gold standard for nondestructive 3D imaging of biomedical samples in the centimeter scale, but it has limited effectiveness in revealing intricate soft tissue details due to the limited attenuation contrast. Radiopaque contrast agents that accumulate in the structures of interest are employed to enhance their visibility. However, the increased attenuation provided by the contrast agents does not guarantee discrimination among tissues. This issue can be solved by spectral μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT (Sμ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT) systems employing small-pixel chromatic photon-counting detectors. These detectors, combined with material decomposition algorithms, allow the generation of high-resolution material-specific 3D maps. This work aims to demonstrate the potential of photon-counting X-ray Sμ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT on osteochondral samples loaded with a cationic iodinated contrast agent (CA4+) at a spatial resolution below 50 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m, and to compare the results against a conventional μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT system. An osteochondral sample extracted from a bovine stifle joint was loaded with CA4+ and imaged with a novel multimodal X-ray imaging system, featuring a 62 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m pixel CdTe spectral detector (Pixirad1-PixieIII). After material decomposition, quantitative 3D density maps of iodine and hydroxyapatite were reconstructed. The same sample was also scanned with a commercial μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner with matched spectrum and exposure time. SCTμ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document} images at a (measured) spatial resolution comparable with the commercial scanner (∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}45 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m) were obtained. Spectral images allowed for a fully automatic segmentation of cartilage and subchondral bone. The unambiguous discrimination between iodine and hydroxyapatite revealed a more realistic representation of proteoglycan distribution compared to conventional imaging.

Performance Verification of Integrated Module for Automatic Analysis of Digital Maps in Earthwork Sites

Research on digitalization of construction sites using UAV (Unmanned Aerial Vehicles), UGV (Unmanned Ground Vehicles), and three-dimensional scanners is rapidly progressing. However, most 3D digital map generation and analysis technologies are still performed individually by utilizing libraries and software that require human intervention. In this study, we developed a digital map automatic analysis framework by reviewing five algorithms required for digital map generation and analysis for 3D digital map generation and analysis automation, defining the optimal forward and backward relationship between algorithms. In addition, based on the developed framework, the "digital map automatic analysis integrated module" is developed by considering the scan data format conversion and data processing process for automation. Furthermore, the effectiveness of the development module was verified by automating and speed testing the developed "digital map automatic analysis integrated module".

A review of high-definition map creation methods for autonomous driving

Autonomous driving has been among the most popular and challenging topics in the past few years. Among all modules in autonomous driving, High-definition (HD) map has drawn lots of attention in recent years due to its high precision and informative level in localization. Since localization is a significant module for automated vehicles to navigate an unknown environment, it has immediately become one of the most critical components of autonomous driving. Big organizations like HERE, NVIDIA, and TomTom have created HD maps for different scenes and purposes for autonomous driving. However, such HD maps are not open-source and are only available for internal research or automotive companies. Even though researchers have proposed various methods to create HD maps using different types of sensor data, there are few papers that review and summarize those methods. New researchers do not have a clear insight into the current state of HD map creation methods to work on their HD map research. Due to the reason above, reviewing, classifying, comparing, and summarizing the state-of-the-art techniques for HD map creation is necessary. This paper reviews recent HD map creation methods that leverage both 2D and 3D map generation. This review introduces the concept of HD maps and their usefulness in autonomous driving and gives a detailed overview of HD map creation methods. We will also discuss the limitations of the current HD map creation methods to motivate future research. Additionally, a chronological overview is created with the most recent HD map creation methods in this paper.

Digital Twin-Driven Fault Diagnosis for Autonomous Surface Vehicles

This paper presents a digital twin-driven fault diagnosis approach based on a graphical model and an adaptive extended Kalman filter algorithm for autonomous surface vehicles. In contrast with the traditional adaptive Kalman filter algorithm, where the fault parameters are treated as extended state variables, the newly proposed adaptive extended Kalman filter algorithm estimates the magnitude of the faults by calculating the parameter estimation gains directly from the sensor systems. To this end, the algorithm utilizes data from a global navigation satellite system receiver assuming the persistence of excitation conditions on the control inputs. The algorithm is tested in the Otter, an autonomous surface vehicle developed by Maritime Robotics, in which one of its propellers is faulty. Technically, the digital twin receives real-time data from the sensor system, estimates the magnitude of the actuator faults, and visualizes the results in a web-based application using JavaScript with Three.js library and Mapbox for real-world 3D map generation. Information regarding the magnitude of the faults is important for fault-tolerant control. Simulation and experimental results show the proposed approach is able to detect and estimate the actuator faults accurately.

Visually Encoded Contact Inspection System for EMATs

Contact inspections are commonly performed in industry to check for defects and degradation, such as corrosion or cracks. Non-destructive evaluation (NDE) probes are being deployed with increasing frequency using autonomous robots, especially in harsh environments or in areas where access is restricted and difficult. Together with the NDE measurement, it is important to capture the 3D position of the probe so that the location where the data originated is known. This allows for the generation of 3D maps of the inspection, which ensure full scan coverage, and can be used for inspection reports and to generate digital twins of the structure for asset management. In this paper, a full inspection system integrating a robot mountable stereo camera system together with an electromagnetic acoustic transducer (EMAT) probe and a wireless NDE data acquisition system is presented. The system is capable of capturing and merging 3D positional data of the probe as it is scanned and NDE data. The system design in terms of hardware and software is described in this paper. A set of tests to evaluate its performance on relevant structural components are also presented, and the results are reported and discussed.

Chemical Cartography Approaches to Study Trypanosomatid Infection

Pathogen tropism and disease tropism refer to the tissue locations selectively colonized or damaged by pathogens, leading to localized disease symptoms. Human-infective trypanosomatid parasites include Trypanosoma cruzi, the causative agent of Chagas disease; Trypanosoma brucei, the causative agent of sleeping sickness; and Leishmania species, causative agents of leishmaniasis. Jointly, they affect 20 million people across the globe. These parasites show specific tropism: heart, esophagus, colon for T. cruzi, adipose tissue, pancreas, skin, circulatory system and central nervous system for T. brucei, skin for dermotropic Leishmania strains, and liver, spleen, and bone marrow for viscerotropic Leishmania strains. A spatial perspective is therefore essential to understand trypanosomatid disease pathogenesis. Chemical cartography generates 3D visualizations of small molecule abundance generated via liquid chromatography-mass spectrometry, in comparison to microbiological and immunological parameters. This protocol demonstrates how chemical cartography can be applied to study pathogenic processes during trypanosomatid infection, beginning from systematic tissue sampling and metabolite extraction, followed by liquid chromatography-tandem mass spectrometry data acquisition, and concluding with the generation of 3D maps of metabolite distribution. This method can be used for multiple research questions, such as nutrient requirements for tissue colonization by T. cruzi, T. brucei, or Leishmania, immunometabolism at sites of infection, and the relationship between local tissue metabolic perturbation and clinical disease symptoms, leading to comprehensive insight into trypanosomatid disease pathogenesis.

Chemical Cartography Approaches to Study Trypanosomatid Infection.

Pathogen tropism and disease tropism refer to the tissue locations selectively colonized or damaged by pathogens, leading to localized disease symptoms. Human-infective trypanosomatid parasites include Trypanosoma cruzi, the causative agent of Chagas disease; Trypanosoma brucei, thecausative agent of sleeping sickness; and Leishmania species, causative agents of leishmaniasis. Jointly, they affect 20 million people across the globe. These parasites show specific tropism: heart, esophagus, colon for T. cruzi, adipose tissue, pancreas, skin, circulatory system and central nervous system for T. brucei, skin for dermotropic Leishmania strains, and liver, spleen, and bone marrow for viscerotropic Leishmania strains. A spatial perspective is therefore essential to understand trypanosomatid disease pathogenesis. Chemical cartography generates 3D visualizations of small molecule abundance generated via liquid chromatography-mass spectrometry, in comparison to microbiological and immunological parameters. This protocol demonstrates how chemical cartography can be applied to study pathogenic processes during trypanosomatid infection, beginning from systematic tissue sampling and metabolite extraction, followed by liquid chromatography-tandem mass spectrometry data acquisition, and concluding with the generation of 3D maps of metabolite distribution. This method can be used for multiple research questions, such as nutrient requirements for tissue colonization by T. cruzi, T. brucei, or Leishmania, immunometabolism at sites of infection, and the relationship between local tissue metabolic perturbation and clinical disease symptoms, leading to comprehensive insight into trypanosomatid disease pathogenesis.

Procedural Generation of 3D Maps With Snappable Meshes

In this paper we present a technique for procedurally generating 3D maps using a set of premade meshes which snap together based on designer-specified visual constraints. The proposed approach avoids size and layout limitations, offering the designer control over the look and feel of the generated maps, as well as immediate feedback on a given map’s navigability. A prototype implementation of the method, developed in the Unity game engine, is discussed, and a number of case studies are analyzed. These include a multiplayer game where the method was used, together with a number of illustrative examples which highlight various parameterizations and piece selection methods. The technique can be used as a designer-centric map composition method and/or as a prototyping system in 3D level design, opening the door for quality map and level creation in a fraction of the time of a fully human-based approach.

Clustering-Based Plane Segmentation Neural Network for Urban Scene Modeling.

Urban scene modeling is a challenging but essential task for various applications, such as 3D map generation, city digitization, and AR/VR/metaverse applications. To model man-made structures, such as roads and buildings, which are the major components in general urban scenes, we present a clustering-based plane segmentation neural network using 3D point clouds, called hybrid K-means plane segmentation (HKPS). The proposed method segments unorganized 3D point clouds into planes by training the neural network to estimate the appropriate number of planes in the point cloud based on hybrid K-means clustering. We consider both the Euclidean distance and cosine distance to cluster nearby points in the same direction for better plane segmentation results. Our network does not require any labeled information for training. We evaluated the proposed method using the Virtual KITTI dataset and showed that our method outperforms conventional methods in plane segmentation. Our code is publicly available.

An Algorithm for Local Dynamic Map Generation for Safe UAV Navigation

For safe UAV navigation and to avoid collision, it is essential to have accurate and real-time perception of the environment surrounding the UAV, such as free area detection and recognition of dynamic and static obstacles. The perception system of the UAV needs to recognize information such as the position and velocity of all objects in the surrounding local area regardless of the type of object. At the same time, a probability based representation taking into account the noise of the sensor is also essential. In addition, a software design with efficient memory usage and operation time is required in consideration of the hardware limitations of the UAVs. In this paper, we propose a 3D Local Dynamic Map (LDM) generation algorithm for a perception system for UAVs. The proposed LDM uses a circular buffer as a data structure to ensure low memory usage and fast operation speed. A probability based occupancy map is created using sensor data and the position and velocity of each object are calculated through clustering between grid voxels using the occupancy map and velocity estimation based on a particle filter. The objects are predicted using the position and velocity of each object and this is reflected in the occupancy map. This process is continuously repeated and the flying environment of the UAV can be expressed in a three-dimensional grid map and the state of each object. For the evaluation of the proposed LDM, we constructed simulation environments and the UAV for outdoor flying. As an evaluation factor, the occupancy grid is accuracy evaluated and the ground truth velocity and the estimated velocity are compared.

Assessing diversity, abundance, and mass of microplastics (~ 1–300 μm) in aquatic systems

AbstractRecent improvements in survey methods for microplastic (MP) pollution in natural waters have enabled quantification of MP particles < 300 μm in diameter. Historically, this putatively important size fraction of MP particles has not been sampled adequately, possibly leading to significant underestimations of plastic inventories in aquatic systems. Furthermore, reported MP particle numbers are poorly constrained proxies for MP mass loadings as sizes vary widely and densities differ within and among polymers, consequently compromising mass flux estimations. However, determination of MP number, identity, and mass can resolve these issues and enable more accurate estimations of MP loadings. Using Raman microspectroscopy coupled with three‐dimensional (3D) spectral imaging, we have developed an automatable methodology to detect, identify, and quantify MPs retained on filters from discrete water samples. Raman spectral information enabled identification of plastic polymers in particles between 0.5 and 300 μm in diameter and generation of 3D chemical maps. To accelerate volume determinations of each identified MP particle, we developed an algorithm to derive volumes from image analysis of two‐dimensional micrographs that are statistically indistinguishable from those directly measured by 3D Raman chemical maps. Masses of each Raman‐identified MP particle are determined from their volumes and known plastic specific weights. This methodology provides accurate estimations of the smallest MP particles' contributions to total MP mass in discrete water samples, providing vital information for mass flux calculations of MPs in natural waters.

Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network

Stereoscopic image retargeting aims at converting stereoscopic images to the target resolution adaptively. Different from 2D image retargeting, stereoscopic image retargeting needs to preserve both the shape structure of salient objects and depth consistency of 3D scenes. In this paper, we present a stereoscopic image retargeting method based on deep convolutional neural network to obtain high-quality retargeted images with both object shape preservation and scene depth preservation. First, a cross-attention extraction mechanism is constructed to generate attention map, which contains the valuable attention features of the left and right images and the common attention features between them. Second, since the disparity map can provide accurate depth information of objects in 3D scenes, a disparity-assisted 3D significance map generation module is utilized to further preserve the valuable depth information of stereoscopic images. Finally, in order to predict the retargeted stereoscopic images accurately, an image consistency loss is developed to preserve the geometric structure of salient objects, and a disparity consistency loss is introduced to eliminate depth distortions. Experimental results demonstrate that the proposed deep convolutional neural network can provide favorable stereoscopic image retargeting results.

ARDEA—An MAV with skills for future planetary missions

AbstractWe introduce a prototype flying platform for planetary exploration: autonomous robot design for extraterrestrial applications (ARDEA). Communication with unmanned missions beyond Earth orbit suffers from time delay, thus a key criterion for robotic exploration is a robot's ability to perform tasks without human intervention. For autonomous operation, all computations should be done on‐board and Global Navigation Satellite System (GNSS) should not be relied on for navigation purposes. Given these objectives ARDEA is equipped with two pairs of wide‐angle stereo cameras and an inertial measurement unit (IMU) for robust visual‐inertial navigation and time‐efficient, omni‐directional 3D mapping. The four cameras cover a vertical field of view, enabling the system to operate in confined environments such as caves formed by lava tubes. The captured images are split into several pinhole cameras, which are used for simultaneously running visual odometries. The stereo output is used for simultaneous localization and mapping, 3D map generation and collision‐free motion planning. To operate the vehicle efficiently for a variety of missions, ARDEA's capabilities have been modularized into skills which can be assembled to fulfill a mission's objectives. These skills are defined generically so that they are independent of the robot configuration, making the approach suitable for different heterogeneous robotic teams. The diverse skill set also makes the micro aerial vehicle (MAV) useful for any task where autonomous exploration is needed. For example terrestrial search and rescue missions where visual navigation in GNSS‐denied indoor environments is crucial, such as partially collapsed man‐made structures like buildings or tunnels. We have demonstrated the robustness of our system in indoor and outdoor field tests.

Modeling the Electromagnetic Scattering Characteristics of Carbon Nanotube Composites Characterized by 3-D Tomographic Transmission Electron Microscopy

In all prior electromagnetic modeling studies of carbon nanotube (CNT) composites, the exact three-dimensional (3D) shape and spatial distribution of the CNTs in the composite were unknown. Therefore, simplifying assumptions had to be made regarding the CNT distributions. The effect of such assumptions on the electromagnetic response of CNT composites has not been quantified. Recent advances in electron-tomography and image analysis have allowed the generation of 3D maps of multi-walled carbon nanotube (MWCNT) distributions with sub-nanometer resolution. In this work, the electromagnetic responses of experimentally mapped 3D structures of aligned-CNT polymer nanocomposites were calculated using both full-wave electromagnetic solvers and dilute-limit Effective Medium Approximations (EMA). Our results show that the electromagnetic response calculated using the full-wave solver exhibits additional resonances that are absent in the response calculated using the dilute-limit EMA. This difference is due to the strong electromagnetic coupling between adjacent MWCNTs, within five CNT radii, of each other. Using the mapped 3D MWCNTs, we also studied the anisotropy in the electromagnetic response of the composites and showed that it increases with the MWCNT volume fraction. The full-wave analysis presented in this work provides a more accurate understanding of the electromagnetic reflection and anisotropy of CNT composites.

Feature coordinate estimation by time differenced vision/GPS/INS

This paper proposes a multi-baseline method to estimate absolute coordinates of point clouds and the camera attitude parameter utilizing feature points in successive images. Conventionally, 3D map generation methodologies have been based on images acquired by aerial or land vehicles. Pixels corresponding to known landmarks are manually identified at first. Next, the coordinates are directly geo-referenced and automatically allocated to pixels with high-quality Global Positioning System (GPS) and Inertial Navigation System (INS). However, it is difficult to obtain accurate coordinates by the conventional methodology with low-cost GPS and INS. With camera positions and attitude parameters, image-based point clouds can be compensated accurately. A simulation was carried out to evaluate the performance of the proposed method.

CisTEM, user-friendly software for single-particle image processing.

We have developed new open-source software called cisTEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging. cisTEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200 k - 300 k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments. cisTEM is available for download from cistem.org.

Multi-distance diffuse optical spectroscopy with a single optode via hypotrochoidal scanning.

Frequency-domain diffuse optical spectroscopy (FD-DOS) is an established technique capable of determining optical properties and chromophore concentrations in biological tissue. Most FD-DOS systems use either manually positioned, handheld probes or complex arrays of source and detector fibers to acquire data from many tissue locations, allowing for the generation of 2D or 3D maps of tissue. Here, we present a new method to rapidly acquire a wide range of source-detector (SD) separations by mechanically scanning a single SD pair. The source and detector fibers are mounted on a scan head that traces a hypotrochoidal pattern over the sample that, when coupled with a high-speed FD-DOS system, enables the rapid collection of dozens of SD separations for depth-resolved imaging. We demonstrate that this system has an average error of 4±2.6% in absorption and 2±1.8% in scattering across all SD separations. Additionally, by linearly translating the device, the size and location of an absorbing inhomogeneity can be determined through the generation of B-scan images in a manner conceptually analogous to ultrasound imaging. This work demonstrates the potential of single optode diffuse optical scanning for depth resolved visualization of heterogeneous biological tissues at near real-time rates.

3D object detection with deep learning

Finding an appropriate environment representation is a crucial problem in robotics. 3D data has been recently used thanks to the advent of low cost RGB-D cameras. We propose a new way to represent a 3D map based on the information provided by an expert. Namely, the expert is the output of a Convolutional Neural Network trained with deep learning techniques. Relying on such information, we propose the generation of 3D maps using individual semantic labels, which are associated with environment objects or semantic labels. So, for each label we are provided with a partial 3D map whose data belong to the 3D perceptions, namely point clouds, which have an associated probability above a given threshold. The final map is obtained by registering and merging all these partial maps. The use of semantic labels provide us a with way to build the map while recognizing objects.

Architecture for the Automatic Generation of Plans for Multiple UAS from a Generic Mission Description

A planning approach for a platform composed of multiple unmanned aerial systems is presented in this paper. The research activities are focused on the interoperability, task allocation and task planning problems within the system. In order to tackle with the interoperability problem between the vehicles of the platform and other external systems, C-BML has been chosen as the standard language to formalize the description of the missions. Regarding the planning problems involved, several planners have been applied to solve them: a task allocation planner to create an initial assignment of tasks to vehicles, a symbolic planner for high-level reasoning and tools for geometric reasoning. The main contribution of the paper is the use of consolidated task planning techniques to automatically generate low-level plans from a mission described in C-BML, filling the gap between interoperability and automatic plan generation for missions where multiple heterogeneous aerial platforms are involved. The approach has been tested in missions involving multiple surveillance and 3D map generation tasks and the paper includes experimental results.

2A1-U09 移動ロボットによる屋外広域3次元地図の生成

This reserch aims to develop a 3D map generation method by a mobile robot for outdoor environments. A robot moving around in an outdoor envirionment needs to detect obstacles and recognize traversable regions. We develop a 3D map generation method using a 3D laser range finder for a wide outdoor environment. It mainly uses an ICP algorithm for estimating ego-motions. When the ICP algorithm fails, the method uses a plane matching-based ego-motion estimation or visual odometry. When we make a map in a wide area, accumulation of ego-motion estimation errors is not negligible which will distort the resultant map. We thies applied a application of pose graph SLAM to improve the accuracy of the map.