• Title/Summary/Keyword: scan map matching

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Indoor Navigation of a Skid Steering Mobile Robot Via Friction Compensation and Map Matching (마찰 보상과 지도 정합에 의한 미끄럼 조향 이동로봇의 실내 주행)

  • So, Chang Ju;Lyou, Joon
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.5
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    • pp.468-472
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    • 2013
  • This paper deals with the indoor localization problem for a SSMR (Skid Steering Mobile Robot) subjected to wheel-ground friction and with one LRF (Laser Range Finder). In order to compensate for some friction effect, a friction related coefficient is estimated by the recursive least square algorithm and appended to the maneuvering command. Also to reduce odometric information based localization errors, the lines are extracted with scan points of LRF and matched with the ones of the corresponding map built in advance. The present friction compensation and scan map matching schemes have been applied to a laboratory SSMR, and experimental results are given to validate the localization performance along an indoor corridor.

Localization and 3D Polygon Map Building Method with Kinect Depth Sensor for Indoor Mobile Robots (키넥트 거리센서를 이용한 실내 이동로봇의 위치인식 및 3 차원 다각평면 지도 작성)

  • Gwon, Dae-Hyeon;Kim, Byung-Kook
    • Journal of Institute of Control, Robotics and Systems
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    • v.22 no.9
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    • pp.745-752
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    • 2016
  • We suggest an efficient Simultaneous Localization and 3D Polygon Map Building (SLAM) method with Kinect depth sensor for mobile robots in indoor environments. In this method, Kinect depth data is separated into row planes so that scan line segments are on each row plane. After grouping all scan line segments from all row planes into line groups, a set of 3D Scan polygons are fitted from each line group. A map matching algorithm then figures out pairs of scan polygons and existing map polygons in 3D, and localization is performed to record correct pose of the mobile robot. For 3D map-building, each 3D map polygon is created or updated by merging each matched 3D scan polygon, which considers scan and map edges efficiently. The validity of the proposed 3D SLAM algorithm is revealed via experiments.

3D Simultaneous Localization and Map Building (SLAM) using a 2D Laser Range Finder based on Vertical/Horizontal Planar Polygons (2차원 레이저 거리계를 이용한 수직/수평 다각평면 기반의 위치인식 및 3차원 지도제작)

  • Lee, Seungeun;Kim, Byung-Kook
    • Journal of Institute of Control, Robotics and Systems
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    • v.20 no.11
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    • pp.1153-1163
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    • 2014
  • An efficient 3D SLAM (Simultaneous Localization and Map Building) method is developed for urban building environments using a tilted 2D LRF (Laser Range Finder), in which a 3D map is composed of perpendicular/horizontal planar polygons. While the mobile robot is moving, from the LRF scan distance data in each scan period, line segments on the scan plane are successively extracted. We propose an "expected line segment" concept for matching: to add each of these scan line segments to the most suitable line segment group for each perpendicular/horizontal planar polygon in the 3D map. After performing 2D localization to determine the pose of the mobile robot, we construct updated perpendicular/horizontal infinite planes and then determine their boundaries to obtain the perpendicular/horizontal planar polygons which constitute our 3D map. Finally, the proposed SLAM algorithm is validated via extensive simulations and experiments.

Grid Map Building and Sample-based Data Association for Mobile Robot Equipped with Low-Cost IR Sensors (저가 적외선센서를 장착한 이동로봇에 적용 가능한 격자지도 작성 및 샘플기반 정보교합)

  • Kwon, Tae-Bum;Song, Jae-Bok
    • The Journal of Korea Robotics Society
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    • v.4 no.3
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    • pp.169-176
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    • 2009
  • Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot's orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot's pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.

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Map Building Using ICP Algorithm based a Robot Position Prediction (로봇 위치 예측에 기반을 둔 ICP 알고리즘을 이용한 지도 작성)

  • Noh, Sung-Woo;Kim, Tae-Gyun;Ko, Nak-Yong
    • The Journal of the Korea institute of electronic communication sciences
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    • v.8 no.4
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    • pp.575-582
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    • 2013
  • This paper proposes a map building using the ICP algorithm based robot localization prediction. Proposed method predicts a robot location to dead reckoning, makes a map in the ICP algorithm. Existing method makes a map building and robot position using a sensor value of reference data and current data. In this case, a large interval of the difference of the reference data and the current data is difficult to compensate. The proposed method can map correction through practical experiments.

A Study on Stereo Matching Algorithm using Disparity Space Image (시차공간영상을 이용한 스테레오 영상 정합에 관한 연구)

  • Lee, Jong-Min;Kim, Dae-Hyun;Choi, Jong-Soo
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.6
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    • pp.9-18
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    • 2004
  • This paper proposes a new and simple stereo matching algorithm using the disparity space image (DSI) technique. First of all, we detect some salient feature points on each scan-line of the image pair and set the matching area using those points and define a simple cost matrix. And we take advantage of matching by pixel-by-pixel instead of using the matching window. While the pixel-by-pixel method boost up the speed of matching, because of no using neighbor information, the correctness of the matching may not be better. To cover this point, we expand the matching path using character of disparity-space-image for using neighbor information. In addition, we devise the compensated matching module using the volume of the disparity space image in order to improve the accuracy of the match. Consequently, we can reduce mismatches at the disparity discontinuities and can obtain the more detailed and correct disparity map.

Considerations for Developing a SLAM System for Real-time Remote Scanning of Building Facilities (건축물 실시간 원격 스캔을 위한 SLAM 시스템 개발 시 고려사항)

  • Kang, Tae-Wook
    • Journal of KIBIM
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    • v.10 no.1
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    • pp.1-8
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    • 2020
  • In managing building facilities, spatial information is the basic data for decision making. However, the method of acquiring spatial information is not easy. In many cases, the site and drawings are often different due to changes in facilities and time after construction. In this case, the site data should be scanned to obtain spatial information. The scan data actually contains spatial information, which is a great help in making space related decisions. However, to obtain scan data, an expensive LiDAR (Light Detection and Ranging) device must be purchased, and special software for processing data obtained from the device must be available.Recently, SLAM (Simultaneous localization and mapping), an advanced map generation technology, has been spreading in the field of robotics. Using SLAM, 3D spatial information can be obtained quickly in real time without a separate matching process. This study develops and tests whether SLAM technology can be used to obtain spatial information for facility management. This draws considerations for developing a SLAM device for real-time remote scanning for facility management. However, this study focuses on the system development method that acquires spatial information necessary for facility management through SLAM technology. To this end, we develop a prototype, analyze the pros and cons, and then suggest considerations for developing a SLAM system.

A study on the real time obstacle recognition by scanned line image (스캔라인 연속영상을 이용한 실시간 장애물 인식에 관한 연구)

  • Cheung, Sheung-Youb;Oh, Jun-Ho
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.21 no.10
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    • pp.1551-1560
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    • 1997
  • This study is devoted to the detection of the 3-dimensional point obstacles on the plane by using accumulated scan line images. The proposed accumulating only one scan line allow to process image at real time. And the change of motion of the feature in image is small because of the short time between image frames, so it does not take much time to track features. To obtain recursive optimal obstacles position and robot motion along to the motion of camera, Kalman filter algorithm is used. After using Kalman filter in case of the fixed environment, 3-dimensional obstacles point map is obtained. The position and motion of moving obstacles can also be obtained by pre-segmentation. Finally, to solve the stereo ambiguity problem from multiple matches, the camera motion is actively used to discard mis-matched features. To get relative distance of obstacles from camera, parallel stereo camera setup is used. In order to evaluate the proposed algorithm, experiments are carried out by a small test vehicle.

Reliable Autonomous Reconnaissance System for a Tracked Robot in Multi-floor Indoor Environments with Stairs (다층 실내 환경에서 계단 극복이 가능한 궤도형 로봇의 신뢰성 있는 자율 주행 정찰 시스템)

  • Juhyeong Roh;Boseong Kim;Dokyeong Kim;Jihyeok Kim;D. Hyunchul Shim
    • The Journal of Korea Robotics Society
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    • v.19 no.2
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    • pp.149-158
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    • 2024
  • This paper presents a robust autonomous navigation and reconnaissance system for tracked robots, designed to handle complex multi-floor indoor environments with stairs. We introduce a localization algorithm that adjusts scan matching parameters to robustly estimate positions and create maps in environments with scarce features, such as narrow rooms and staircases. Our system also features a path planning algorithm that calculates distance costs from surrounding obstacles, integrated with a specialized PID controller tuned to the robot's differential kinematics for collision-free navigation in confined spaces. The perception module leverages multi-image fusion and camera-LiDAR fusion to accurately detect and map the 3D positions of objects around the robot in real time. Through practical tests in real settings, we have verified that our system performs reliably. Based on this reliability, we expect that our research team's autonomous reconnaissance system will be practically utilized in actual disaster situations and environments that are difficult for humans to access, thereby making a significant contribution.

Development of an Automatic 3D Coregistration Technique of Brain PET and MR Images (뇌 PET과 MR 영상의 자동화된 3차원적 합성기법 개발)

  • Lee, Jae-Sung;Kwark, Cheol-Eun;Lee, Dong-Soo;Chung, June-Key;Lee, Myung-Chul;Park, Kwang-Suk
    • The Korean Journal of Nuclear Medicine
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    • v.32 no.5
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    • pp.414-424
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    • 1998
  • Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.

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