The DOI matches its Crossref record; 13 of the 17 references it cites were found in a registry or an index.
Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping
Abstract
Three-dimensional perception is a key technology for many robotics applications, including obstacle detection, mapping, and localization. There exist a number of sensors and techniques for acquiring 3-D data, many of which have particular utility for various robotic tasks. We introduce a new design for a 3-D sensor system, constructed from a 2-D range scanner coupled with a passive linkage mechanism, such as a spring. By mounting the other end of the passive linkage mechanism to a moving body, disturbances resulting from accelerations and vibrations of the body propel the 2-D scanner in an irregular fashion, thereby extending the device's field of view outside of its standard scanning plane. The proposed 3-D sensor system is advantageous due to its mechanical simplicity, mobility, low weight, and relatively low cost. We analyze a particular implementation of the proposed device, which we call Zebedee, consisting of a 2-D time-of-flight laser range scanner rigidly coupled to an inertial measurement unit and mounted on a spring. The unique configuration of the sensor system motivates unconventional and specialized algorithms to be developed for data processing. As an example application, we describe a novel 3-D simultaneous localization and mapping solution in which Zebedee is mounted on a moving platform. Using a motion capture system, we have verified the positional accuracy of the sensor trajectory. The results demonstrate that the six-degree-of-freedom trajectory of a passive spring-mounted range sensor can be accurately estimated from laser range data and industrial-grade inertial measurements in real time and that a quality 3-D point cloud map can be generated concurrently using the same data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Bosse et al. [21] extended the continuous 3D scan-matching formulation from [19] to a large-scale SLAM application.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [21]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
CHOMP: Gradient optimization techniques for efficient motion planning
Ratliff, N., Zucker, M., Bagnell, J. A., & Srinivasa, S. S. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152817 · Cited by 1,052
Juang, J.-N. (1994) · Medical Entomology and Zoology · Cited by 1,046
RHex: A Biologically Inspired Hexapod Runner
Altendorfer, R., Moore, N., Komsuoglu, H., Buehler, M., Brown, H. B., McMordie, D., Saranlı, U., Full, R. J., & Koditschek, D. E. (2001) · Autonomous Robots · doi:10.1023/a:1012426720699 · Cited by 412
Continuous 3D scan-matching with a spinning 2D laser
Bosse, M., & Zlot, R. (2009) · 2009 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2009.5152851 · Cited by 289
Self-citationA robust approach to high‐speed navigation for unrehearsed desert terrain
Urmson, C., Ragusa, C., Ray, D., Anhalt, J., Bartz, D., Galatali, T., Gutierrez, A., Johnston, J., Harbaugh, S., Kato, H. “., et al. (2006) · Journal of Field Robotics · doi:10.1002/rob.20126 · Cited by 145
A Robust Approach to High-Speed Navigation for Unrehearsed Desert Terrain
Urmson, C., Anhalt, J., Bartz, D., Clark, M., Galatali, T., Gutierrez, A., Harbaugh, S., Johnston, J., Kato, H. “., Koon, P., et al. (2007) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-540-73429-1_2 · Cited by 139
Characterization of the compact Hokuyo URG-04LX 2D laser range scanner
Kneip, L., Tâche, F., Caprari, G., & Siegwart, R. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152579 · Cited by 125
Error modeling and calibration of exteroceptive sensors for accurate mapping applications
Underwood, J., Hill, A. J., Peynot, T., & Scheding, S. (2009) · Journal of Field Robotics · doi:10.1002/rob.20315 · Cited by 65
Wellington, C., & Stentz, A. (2006) · Springer tracts in advanced robotics · doi:10.1007/10991459_9 · Cited by 43
TICSync: Knowing when things happened
Harrison, A., & Newman, P. M. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980112 · Cited by 38
Research works printed but not linked: 2
- wulf, 2003, Fast 3D scanning methods for laser measurement systems, Proc Int Conf Control Syst Comput Sci, 312
- petrie, 2010, Mobile mapping systems: An introduction to the technology, GeoInformatics, 13, 32
Cited by
Sorted by citations
Items 201–300 of 399
Li, N., Hu, Z., & Zhao, B. (2016) · Applied Optics · doi:10.1364/ao.55.002270 · Cited by 15
UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER
Hillemann, M., & Jutzi, B. (2017) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-2-w3-17-2017 · Cited by 15
Probabilistic Approach for Visual Homing of a Mobile Robot in the Presence of Dynamic Obstacles
Sabnis, A., Arunkumar, G. K., Dwaracherla, V., & Vachhani, L. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2569496 · Cited by 14
Continuous latent state preintegration for inertial-aided systems
Gentil, C. L., & Vidal‐Calleja, T. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231199537 · Cited by 14
Local quality assessment of point clouds for indoor mobile mapping
Huang, F., Wen, C., Luo, H., Cheng, M., Wang, C. Q., & Li, J. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.02.033 · Cited by 14
Real-time 6DoF localization for a mobile robot using pre-computed 3D laser likelihood field
Ohsato, A., Sasaki, Y., & Mizoguchi, H. (2015) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2015.7419127 · Cited by 13
INTEGRATING A LOW-COST MEMS IMU INTO A LASER-BASED SLAM FOR INDOOR MOBILE MAPPING
Karam, S., Lehtola, V., & George Vosselman, A. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w17-149-2019 · Cited by 13
Vogel, S., Alkhatib, H., & Neumann, I. (2018) · International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455258 · Cited by 13
Smart heritage: Challenges in digitisation and spatial information modelling of historical buildings
Khoshelham, K. (2018) · CEUR Workshop Proceedings · Cited by 13
An Automatic Tunnel Shotcrete Robot
Lin, X., Song, D., Qin, M., Zhang, W., He, X., & Xie, B. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8996350 · Cited by 13
GLO-SLAM: a slam system optimally combining GPS and LiDAR odometry
Lin, R., Xu, J.-Z., & Zhang, J. (2021) · Industrial robot · doi:10.1108/ir-12-2020-0272 · Cited by 12
Structure-From-Motion in 3D Space Using 2D Lidars
Choi, D., Bok, Y., Kim, J.-S., Shim, I., & Kweon, I. S. (2017) · Sensors · doi:10.3390/s17020242 · Cited by 13
Real-time depth and inertial fusion for local SLAM on dynamic legged robots
Camurri, M., Bazeille, S., Caldwell, D. G., & Semini, C. (2015) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi.2015.7295818 · Cited by 12
Li, M., Hu, K., Liu, Y., Hu, E., Tang, C., Zhu, H. J., & Zhou, G. (2023) · Remote Sensing · doi:10.3390/rs15215093 · Cited by 12
Indoor Multi-Sensor Acquisition System for Projects on Energy Renovation of Buildings
Armesto, J., Sánchez-Villanueva, C., Cambeiro, F. P., & Patiño-Barbeito, F. (2016) · Sensors · doi:10.3390/s16060785 · Cited by 12
STANDALONE TERRESTRIAL LASER SCANNING FOR EFFICIENTLY CAPTURING AEC BUILDINGS FOR AS-BUILT BIM
Bassier, M., Vergauwen, M., & Genechten, B. V. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iii-6-49-2016 · Cited by 12
Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems
Paynter, I. L., Genest, D. M., Peri, F., & Schaaf, C. (2018) · Interface Focus · doi:10.1098/rsfs.2017.0043 · Cited by 12
Mapping in unstructured natural environment: a sensor fusion framework for wearable sensor suites
Chahine, G., Vaidis, M., Pomerleau, F., & Pradalier, C. (2021) · SN Applied Sciences · doi:10.1007/s42452-021-04555-y · Cited by 12
Goelles, T., Hammer, T., Muckenhuber, S., Schlager, B., Abermann, J., Bauer, C., Jimenez, V. J. E., Schöner, W., Schratter, M., Schrei, B., et al. (2022) · Geoscientific instrumentation, methods and data systems · doi:10.5194/gi-11-247-2022 · Cited by 12
Almqvist, H., Magnusson, M., & Lilienthal, A. J. (2013) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-013-9957-9 · Cited by 12
Towards online mobile mapping using inhomogeneous lidar data
Vlaminck, M., Luong, H. Q., Goeman, W., Veelaert, P., & Philips, W. R. (2016) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2016.7535486 · Cited by 11
AN EVALUATION PIPELINE FOR INDOOR LASER SCANNING POINT CLOUDS
Karam, S., Peter, M., Hosseinyalamdary, S., & George Vosselman, A. (2018) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-1-85-2018 · Cited by 11
Sun, W., Wang, J., Jin, F., & Yang, Y. (2020) · International Journal of Remote Sensing · doi:10.1080/2150704x.2020.1811911 · Cited by 11
Convex Cut: A realtime pseudo-structure extraction algorithm for 3D point cloud data
Jun, C., Youn, J., Choi, J., Medioni, G. G., & Doh, N. L. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353929 · Cited by 11
Automated image-based reconstruction of building interiors – a case study
Lehtola, V., Kurkela, M., & Hyyppä, H. (2014) · The Photogrammetric Journal of Finland/The photogrammetric journal of Finland · doi:10.17690/0414241.1 · Cited by 11
The 3D laser range finder design for the navigation and mapping for the coal mine robot
Olivka, P., Mihola, M., Novák, P., Kot, T., & Babjak, J. (2016) · International Carpathian Control Conference · doi:10.1109/carpathiancc.2016.7501155 · Cited by 11
Low cost solution for 3D mapping of environment using 1D LIDAR for autonomous navigation
Athavale, R., Ram, D. S. H., & Nair, B. B. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/561/1/012104 · Cited by 11
An RPLiDAR based SLAM equipped with IMU for Autonomous Navigation of Wheeled Mobile Robot
Aslam, M. S., Aziz, M. I., Naveed, K., & Zaman, U. K. U. (2020) · IEEE International Multitopic Conference (INMIC) · doi:10.1109/inmic50486.2020.9318133 · Cited by 11
Evaluation of data acquisition techniques and workflows for Scan to BIM
Bassier, M., Yousefzadeh, M., & Genechten, B. V. (2015) · Lirias · Cited by 11
UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT
Petrlík, M., Petráček, P., Krátký, V., Musil, T., Stasinchuk, Y., Vrba, M., Báča, T., Heřt, D., Pecka, M., Svoboda, T., et al. (2024) · IEEE transactions on field robotics. · doi:10.1109/tfr.2024.3492160 · Cited by 10
Representation of 3D Environment Map Using B-Spline Surface with Two Mutually Perpendicular LRFs
Yan, R.-J., Wu, J. B., Lee, J. Y., & Han, C.-S. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/690310 · Cited by 10
Art and Science as Creative Catalysts
Gates-Stuart, E., Nguyen, C. V., Adcock, M., Bradley, J., Morell, M. K., & Lovell, D. R. (2015) · Leonardo · doi:10.1162/leon_a_01065 · Cited by 10
Dean, C., Kirkpatrick, J., Osborn, J., Doyle, R., Fitzgerald, N. B., & Roxburgh, S. H. (2018) · AoB Plants · doi:10.1093/aobpla/ply015 · Cited by 10
Moving Humans Removal for Dynamic Environment Reconstruction from Slow-Scanning LIDAR Data
Zhang, T., & Nakamura, Y. (2018) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/urai.2018.8441778 · Cited by 10
Slam-Based Multi-Sensor Backpack Lidar Systems in Gnss-Denied Environments
Zhang, D., Gong, Z., Chen, Y., Zelek, J., & Li, J. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8898669 · Cited by 10
Będkowski, J., Nowak, H., Kubiak, B., Studzinski, W., Janeczek, M., Karas, S., Kopaczewski, A., Makosiej, P., Koszuk, J., Pec, M., et al. (2021) · Sensors · doi:10.3390/s21175691 · Cited by 10
Applying a Hand-Held Laser Scanner to Monitoring Gully Erosion: Workflow and Evaluation
Henderson, A., Hawdon, A., Bartley, R., Wilkinson, S. N., & Löwe, T. (2021) · Remote Sensing · doi:10.3390/rs13194004 · Cited by 10
Xiao, K., Yu, W., Liu, W., Qu, F. J., & Ma, Z. (2022) · Applied Sciences · doi:10.3390/app12030939 · Cited by 10
RADLER - A RADial LasER Scanning Device
Borrmann, D., Jörissen, S., & Nüchter, A. (2020) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-33950-0_56 · Cited by 9
Ishikawa, R., Roxas, M., Satō, Y., Oishi, T., Masuda, T., & Ikeuchi, K. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.70 · Cited by 9
Towards an efficient 3D model estimation methodology for aerial and ground images
Potje, G., de Resende, G. D., Campos, M. F. M., & Nascimento, E. R. (2017) · Machine Vision and Applications · doi:10.1007/s00138-017-0875-x · Cited by 9
INDOOR AND OUTDOOR MOBILE MAPPING SYSTEMS FOR ARCHITECTURAL SURVEYS
Campi, M., di Luggo, A., Mónaco, S., Siconolfi, M., & Palomba, D. (2018) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-201-2018 · Cited by 9
Comparative Study of Single-Wood Biomass Model at Plot Level Based on Multi-Source LiDAR
Zhang, Y., Xue, S., Liu, S., Li, X., Fan, Q., Xiong, N., & Wang, J. (2024) · Forests · doi:10.3390/f15050795 · Cited by 8
Deep Networks and Sensor Fusion for Personal Care Robot Tasks—A Review
Ramsai, N., & Sridharan, K. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3530007 · Cited by 8
Plane-based scan registration with moving vehicles exclusion
Wei, C., Wang, R.-L., Wu, T., Chen, T., Fang, Y., & Fu, H. (2016) · Robotics Auton. Syst. · doi:10.1016/j.robot.2016.05.007 · Cited by 7
THE 4DILAN PROJECT (4TH DIMENSION IN LANDSCAPE AND ARTIFACTS ANALYSES)
Chiabrando, F., Naretto, M., Sammartano, G., Sambuelli, L., Spano, A. T., & Losè, L. T. (2017) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-5-w1-227-2017 · Cited by 8
Hasler, O. K., Blaser, S., & Nebiker, S. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2020-741-2020 · Cited by 8
Towards spherical robots for mobile mapping in human made environments
Arzberger, F., Bredenbeck, A., Zevering, J., Borrmann, D., & Nüchter, A. (2021) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2021.100004 · Cited by 8
3D BUILDING FAÇADE RECONSTRUCTION USING HANDHELD LASER SCANNING DATA
Sadeghi, F., Arefi, H., Fallah, A., & Hahn, M. (2015) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-1-w5-625-2015 · Cited by 8
Towards a novel auto-rotating lidar platform for cavity surveying
Mitchell, J., & Marshall, J. A. (2020) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2019.103260 · Cited by 8
A Unified 3D Mapping Framework Using a 3D or 2D LiDAR
Zhen, W., & Scherer, S. (2020) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-33950-0_60 · Cited by 8
Fan, W., Liu, H., Xu, Y., & Lin, W. (2021) · Scandinavian Journal of Forest Research · doi:10.1080/02827581.2021.1973554 · Cited by 8
Aspects of Cave Data Use in a GIS
Albert, G. (2017) · InTech eBooks · doi:10.5772/intechopen.68833 · Cited by 8
Olivka, P., Mihola, M., Novák, P., Kot, T., & Babjak, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47605-6_30 · Cited by 7
Design, Dynamics Modelling and Control of a H-Shape Multi-rotor System for Indoor Navigation
Phang, S. K., Ahmed, S. Z., & Hamid, M. R. A. (2019) · International Conference on Unmanned Vehicle Systems-Oman (UVS) · doi:10.1109/uvs.2019.8658354 · Cited by 7
Hasler, O. K., Blaser, S., & Nebiker, S. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w17-135-2019 · Cited by 7
3D Object Recognition and Localization with a Dense LiDAR Scanner
Geng, H., Gao, Z., Fang, G., & Xie, Y. (2022) · Actuators · doi:10.3390/act11010013 · Cited by 7
Perfetti, L., Elalailyi, A., & Fassi, F. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2022-259-2022 · Cited by 7
Evaluation of a ToF camera for remote surveying of underground cavities excavated by jet boring
Ingram, C., & Marshall, J. A. (2014) · Automation in Construction · doi:10.1016/j.autcon.2014.08.013 · Cited by 7
López, J. A. B., & Vera, J. A. B. (2020) · Arqueología de la Arquitectura · doi:10.3989/arq.arqt.2020.004 · Cited by 7
Chen, Z., Li, Q., Li, J., Zhang, D., Yu, J., Yin, Y., Lv, S., & Liang, A. (2022) · Remote Sensing · doi:10.3390/rs14061365 · Cited by 7
4-D SLAM: An Efficient Dynamic Bayes Network-Based Approach for Dynamic Scene Understanding
Wang, Z., Li, W., Shen, Y., & Cai, B. (2020) · IEEE Access · doi:10.1109/access.2020.3042339 · Cited by 7
Reconstruction of rigid body models from motion distorted laser range data using optical flow
Ilg, E., Kuummerle, R., Burgard, W., & Brox, T. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907535 · Cited by 6
A Real-time and Low-cost 3D SLAM System Based on a Continuously Rotating 2D Laser Scanner
Fang, Z., Zhao, S., & Wen, S. (2017) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber.2017.8446162 · Cited by 6
A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application
Wang, G., Wei, X., Chen, Y., Zhang, T., Hou, M., & Liu, Z. (2022) · Remote Sensing · doi:10.3390/rs14225877 · Cited by 6
2D-3D hybrid mapping for path planning in autonomous robots
Megalingam, R. K., Tantravahi, S., Tammana, H. S. S. K., & Puram, H. S. R. (2023) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-023-00272-4 · Cited by 6
A Study of Scan Patterns for Mobile Mapping
Elsberg, J., Borrmann, D., & Nüchter, A. (2013) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-7-w2-75-2013 · Cited by 6
Comparison of Camera and Laser Scanner based 3D Point Cloud
Rehman, Y., Uddin, H. M. A., Siddique, T. H. M., Haris, Jafri, S. R. U. N., & Ahmed, A. F. (2019) · International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST) · doi:10.1109/iceest48626.2019.8981708 · Cited by 6
Long-Arm Three-Dimensional LiDAR for Antiocclusion and Antisparsity Point Clouds
Lin, J., Li, S.-Q., Dong, W., Matsumaru, T., & Xie, S. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3104019 · Cited by 6
The Applicability of the Rapid Handheld Laser Scanner to Underground Tunnel Suveying
Makkonen, T., Heikkilä, R., Kaaranka, A., & Naatsaari, M. (2015) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.22260/isarc2015/0107 · Cited by 6
3D Map Building Using Mobile Robot with Scan Device
Kita, S., Lee, J. H., & Okamoto, S. (2016) · International Journal of Mechanical Engineering and Robotics Research · doi:10.18178/ijmerr.5.3.191-195 · Cited by 6
Automatic 2-D LiDAR geometric calibration of installation bias
Guerreiro, B. J., Silvestre, C., & Oliveira, P. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.05.001 · Cited by 6
Tiede, J., Reinke, K. J., & Jones, S. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102675 · Cited by 5
Cheng, H., & Han, J. (2024) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-03-2024-0132 · Cited by 5
A high frequency 3D LiDAR with enhanced measurement density via Papoulis-Gerchberg
Özbay, B., Hıdır, E. K., Gul, M., Öztürk, D., Taşcı, M., Arisoy, A. M., Sirin, H. O., & Uyanık, İ. (2015) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2015.7251509 · Cited by 5
SAGE: Semantic Annotation of Georeferenced Environments
Moghadam, P., Evans, B., & Duff, E. (2015) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-015-0302-3 · Cited by 5
Forest mapping and trunk parameter measurement on slope using a 3D-LIDAR
Seki, S., Tsubouchi, T., Saratat, S., & Hara, Y. (2017) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2017.8279242 · Cited by 5
Scale‐aware camera localization in 3D LiDAR maps with a monocular visual odometry
Sun, M., Yang, S., & Liu, H. (2019) · Computer Animation and Virtual Worlds · doi:10.1002/cav.1879 · Cited by 6
A single-shot pose estimation approach for a 2D laser rangefinder
Li, Y., Hu, Z., Li, Z., Cai, Y., Sun, S., & Zhou, J. (2019) · Measurement Science and Technology · doi:10.1088/1361-6501/ab455a · Cited by 5
Corrao, R., Paola, F. D., Termini, D., & Vinci, C. (2021) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlvi-m-1-2021-147-2021 · Cited by 5
Dai, Y., Wang, Z., Lin, X., Wen, C., Xu, L., Shen, S., Ma, Y., & Wang, C. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3457229 · Cited by 5
Real-Time Truly-Coupled Lidar-Inertial Motion Correction and Spatiotemporal Dynamic Object Detection
Gentil, C. L., Falque, R., & Vidal‐Calleja, T. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802233 · Cited by 5
Registration of image and 3D LIDAR data from extrinsic calibration
Hu, Z., Hu, Y., Li, Y., & Huang, G. (2015) · International Conference on Transportation Information and Safety · doi:10.1109/ictis.2015.7232189 · Cited by 5
Makkonen, T., Heikkilä, R., Jylänki, J., & Fraser, S. J. (2015) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.22260/isarc2015/0123 · Cited by 5
Efficient Decentralized Collaborative Mapping for Outdoor Environments
Contreras, L., Kermorgant, O., & Martinet, P. (2018) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc.2018.00017 · Cited by 5
Indoor Li-DAR 3D mapping algorithm with semantic-based registration and optimization
Sun, W. J., Liu, L., Ji, X., & Sun, C. (2019) · Soft Computing · doi:10.1007/s00500-019-04482-6 · Cited by 5
Measurement Strategies for Street-Level SLAM Laser Scanning of Urban Environments
Keitaanniemi, A., Kukko, A., Virtanen, J., & Vaaja, M. T. (2020) · The Photogrammetric Journal of Finland/The photogrammetric journal of Finland · doi:10.17690/020271.1 · Cited by 5
Rodríguez‐Martín, M., Rodríguez‐Gonzálvez, P., González‐Aguilera, D., & Nocerino, E. (2018) · IEEE Access · doi:10.1109/access.2018.2884922 · Cited by 5
3D Scan matching for mobile robot localization over rough terrain
Nakagomi, H., Fuse, Y., Hosaka, H., Miyamoto, H., Nakamura, T., Yoneyama, A., Yokotsuka, M., Kamimura, A., Watanabe, H., Tanzawa, T., et al. (2019) · Electrical Engineering in Japan · doi:10.1002/eej.23250 · Cited by 5
Brack, C. L., Schaefer, M., Jovanovic, T., & Crawford, D. F. (2020) · Australian Forestry · doi:10.1080/00049158.2020.1807097 · Cited by 5
Three‐dimensional Scan Registration using Curvelet Features in Planetary Environments
Ahuja, S., Iles, P., & Waslander, S. L. (2015) · Journal of Field Robotics · doi:10.1002/rob.21616 · Cited by 5
Incorporating control inputs in continuous-time Gaussian process state estimation for robotics
Lilge, S., & Barfoot, T. D. (2025) · Robotica · doi:10.1017/s0263574725000116 · Cited by 4
Yu, Y., Yang, G., Zheng, T., Tian, Y., & Li, L. (2017) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/iccis.2017.8274870 · Cited by 4
High‐Precision Motion Compensation for LiDAR Based on LiDAR Odometry
Qin, P., Zhang, C., Ma, X., & Shi, Z. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/5866868 · Cited by 4
Volume Estimation of Raw Materials inside the Silo using Convex Hull Method
Clar, S. E., Aleluya, E. R. M., & Salaan, C. J. O. (2022) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem57413.2022.10109554 · Cited by 4
Reality-Based 3D Survey and Modeling Supporting Historical Vaulted Structures Studies
Spano, A. T., Patrucco, G., & Sammartano, G. (2023) · Lecture notes in civil engineering · doi:10.1007/978-3-031-44328-2_90 · Cited by 4
Singh, M., & Nagla, K. S. (2024) · International Journal of Intelligent Unmanned Systems · doi:10.1108/ijius-08-2024-0235 · Cited by 4
van Leeuwen, M., & Disney, M. I. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-409548-9.10543-3 · Cited by 4
Hu, C., Yang, C., Li, K., & Zhang, J. (2022) · Applied Sciences · doi:10.3390/app12094442 · Cited by 4
3D Mapping Based on Fusion of 2D Laser and IMU Data Acquired by Unmanned Aerial Vehicle
Pozderac, T., Velagić, J., & Osmanković, D. (2019) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit.2019.8820583 · Cited by 4
Metadata
- Authors
- Bosse, M., Zlot, R., & Flick, P.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- IEEE Transactions on Robotics
- Published
- 2012-10
- Volume
- 28
- Issue
- 5
- Pages
- 1104-1119
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2054804188
- Cited by
- 399
- References
- 17
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 18 · 2023: 21 · 2022: 31 · 2021: 39 · 2020: 34 · 2019: 43 · 2018: 41 · 2017: 32 · 2016: 39 · 2015: 40 · 2014: 26 · 2013: 16
- FWCI
- 44.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Robotics (publishedVersion)
- Funders
- Commonwealth Scientific and Industrial Research Organisation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Keywords
- inertial measurement unit, mobile mapping, SLAM, 3D point clouds, low-cost sensor design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review