The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-SLAM
Open accessAbstract
Enabling automated 3D mapping in forests is an important component of the future development of forest technology, and has been garnering interest in the scientific community, as can be seen from the many recent publications. Accordingly, the authors of the present paper propose the use of a Simultaneous Localisation and Mapping algorithm, called graph-SLAM, to generate local maps of forests. In their study, the 3D data required for the mapping process were collected using a custom-made, mobile platform equipped with a number of sensors, including Velodyne VLP-16 LiDAR, a stereo camera, an IMU, and a GPS. The 3D map was generated solely from laser scans, first by relying on laser odometry and then by improving it with robust graph optimisation after loop closures, which is the core of the graph-SLAM algorithm. The resulting map, in the form of a 3D point cloud, was then evaluated in terms of its accuracy and precision. Specifically, the accuracy of the fitted diameter at breast height (DBH) and the relative distance between the trees were evaluated. The results show that the DBH estimates using the Pratt circle fit method could enable a mean estimation error of approximately 2 cm (7–12%) and an RMSE of 2.38 cm (9%), whereas for tree positioning accuracy, the mean error was 0.0476 m. The authors conclude that robust SLAM algorithms can support the development of forestry by providing cost-effective and acceptable quality methods for forest mapping. Moreover, such maps open up the possibility for precision localisation for forestry vehicles.
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.
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
(1996) · Cited by 20,179
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778
Proceedings. 1985 IEEE International Conference on Robotics and Automation
(1985) · doi:10.1109/robot.1985 · Cited by 3,673
Corke, P., Roberts, J., Cunningham, J., & Hainsworth, D. (2008) · Springer Handbook of Robotics · doi:10.1007/978-3-540-30301-5_50 · Cited by 3,543
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · Zaguan (Universidad de Zaragoza) · Cited by 3,153
Screened poisson surface reconstruction
Kazhdan, M., & Hoppe, H. (2013) · ACM Transactions on Graphics · doi:10.1145/2487228.2487237 · Cited by 2,231
Efficient RANSAC for Point‐Cloud Shape Detection
Schnabel, R., Wahl, R., & Klein, R. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01016.x · Cited by 2,106
A Tutorial on Graph-Based SLAM
Grisetti, G., Kummerle, R., Stachniss, C., & Burgard, W. (2010) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2010.939925 · Cited by 1,345
2006 IEEE/Rsj International Conference on Intelligent Robots and Systems
(2006) · 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2006.282202 · Cited by 992
Terrestrial laser scanning in forest inventories
Liang, X., Kankare, V., Hyyppa, J. M., Wang, Y., Kukko, A., Haggren, H. G. A., Yu, X., Kaartinen, H., Jaakkola, A., Guan, F., et al. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.01.006 · Cited by 857
Turner, D., Lucieer, A., & Watson, C. (2012) · Remote Sensing · doi:10.3390/rs4051392 · Cited by 744
Visual odometry for ground vehicle applications
Nistér, D., Naroditsky, O., & Bergen, J. (2006) · Journal of Field Robotics · doi:10.1002/rob.20103 · Cited by 634
Airborne and Terrestrial Laser Scanning
Vosselman, G., & Maas, H. (2010) · Cited by 603
Consistency of the EKF-SLAM Algorithm
Bailey, T., Nieto, J., Guivant, J., Stevens, M., & Nebot, E. (2006) · 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2006.281644 · Cited by 566
Direct least-squares fitting of algebraic surfaces
Pratt, V. (1987) · Proceedings of the 14th annual conference on Computer graphics and interactive techniques · doi:10.1145/37401.37420 · Cited by 468
Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing
Triebel, R., Pfaff, P., & Burgard, W. (2006) · 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2006.282632 · Cited by 347
A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements
Jaakkola, A., Hyyppä, J., Kukko, A., Yu, X., Kaartinen, H., Lehtomäki, M., & Lin, Y. (2010) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.08.002 · Cited by 339
(2009) · Choice Reviews Online · doi:10.5860/choice.46-3272 · Cited by 287
Nonlinear Constraint Network Optimization for Efficient Map Learning
Grisetti, G., Stachniss, C., & Burgard, W. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2009.2026444 · Cited by 222 · cited 1×
“This problem is solved by seeking the configuration of nodes that maximizes the likelihood of the observations encoded in the constraints (Grisetti et al., 2009).”
Simultaneous Localization and Mapping for Forest Harvesters
Miettinen, M., Öhman, M., Visala, A., & Forsman, P. J. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.363838 · Cited by 74
Fearscapes: Mapping Functional Properties of Cover for Prey with Terrestrial LiDAR
Olsoy, P. J., Forbey, J. S., Rachlow, J. L., Nobler, J. D., Glenn, N. F., & Shipley, L. A. (2014) · BioScience · doi:10.1093/biosci/biu189 · Cited by 60
Pierzchała, M., Talbot, B., & Astrup, R. (2014) · Forests · doi:10.3390/f5061212 · Cited by 57
Self-citationTree Stem Diameter Estimation from Mobile Laser Scanning Using Line-Wise Intensity-Based Clustering
Forsman, M., Holmgren, J., & Olofsson, K. (2016) · Forests · doi:10.3390/f7090206 · Cited by 56
Hauglin, M., Gobakken, T., Astrup, R., Ene, L. T., & Næsset, E. (2014) · Forests · doi:10.3390/f5030384 · Cited by 44
Self-citationEnhanced Algorithms for Estimating Tree Trunk Diameter Using 2D Laser Scanner
Ringdahl, O., Hohnloser, P., Hellström, T., Holmgren, J., & Lindroos, O. (2013) · Remote Sensing · doi:10.3390/rs5104839 · Cited by 34
Comparison of Forest Attributes Derived from Two Terrestrial Lidar Systems
Ducey, M. J., Astrup, R., Seifert, S., Pretzsch, H., Larson, B. C., & Coates, K. D. (2013) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.79.3.245 · Cited by 23
Self-citation
Cited by
Evaluating the Forest Ecosystem through a Semi-Autonomous Quadruped Robot and a Hexacopter UAV
Idrissi, M., Hussain, A., Barua, B., Osman, A., Abozariba, R., Aneiba, A., & Asyhari, T. (2022) · Sensors · doi:10.3390/s22155497 · Cited by 25
Metadata
- Authors
- Pierzchała, M., Giguère, P., & Astrup, R.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 145
- Pages
- 217-225
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2782807102
- Cited by
- 258
- References
- 26
- FWCI
- 19.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
