3D Convolutional Neural Networks for landing zone detection from LiDAR
Abstract
We present a system for the detection of small and potentially obscured obstacles in vegetated terrain. The key novelty of this system is the coupling of a volumetric occupancy map with a 3D Convolutional Neural Network (CNN), which to the best of our knowledge has not been previously done. This architecture allows us to train an extremely efficient and highly accurate system for detection tasks from raw occupancy data. We apply this method to the problem of detecting safe landing zones for autonomous helicopters from LiDAR point clouds. Current methods for this problem rely on heuristic rules and use simple geometric features. These heuristics break down in the presence of low vegetation, as they do not distinguish between vegetation that may be landed on and solid objects that should be avoided. We evaluate the system with a combination of real and synthetic range data. We show our system outperforms various benchmarks, including a system integrating various hand-crafted point cloud features from the literature.
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Real and simulated LiDAR data has proven efficient and robust data for neural network training in many applications, such as obstacle detection, path planning, and TRN even in the case of uncooperative operations.
“Real and simulated LiDAR data has proven efficient and robust data for neural network training in many applications, such as obstacle detection20, path planning21, and TRN even in the case of uncooperative operations22.”
From Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning (2024) · cites this paper as [20]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
(2014) · Cited by 33,419 · see citing works
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,222 · see citing works
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works
(1989) · neural information processing systems · Cited by 3,736 · see citing works
Deep learning for detecting robotic grasps
Lenz, I., Lee, H., & Saxena, A. (2013) · Int. J. Robotics Res. · doi:10.1177/0278364914549607 · Cited by 1,820 · see citing works
High resolution maps from wide angle sonar
Moravec, H., & Elfes, A. (2005) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1985.1087316 · Cited by 1,778 · see citing works
Theano: A CPU and GPU Math Compiler in Python
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J. P., Warde-Farley, D., & Bengio, Y. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-003 · Cited by 1,063 · see citing works
A Fast Voxel Traversal Algorithm for Ray Tracing
Amanatides, J., & Woo, A. (1987) · doi:10.2312/egtp.19871000 · Cited by 669 · see citing works
Learning Feature Representations with K-Means
Coates, A., & Ng, A. Y. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_30 · Cited by 627 · see citing works
Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues
Meng, X., Currit, N., & Zhao, K. (2010) · Remote Sensing · doi:10.3390/rs2030833 · Cited by 580 · see citing works
Convolutional-Recursive Deep Learning for 3D Object Classification
Socher, R., Huval, B., Bath, B., Manning, C. D., & Ng, A. Y. (2012) · Cited by 507 · see citing works
Natural terrain classification using three‐dimensional ladar data for ground robot mobility
Lalonde, J., Vandapel, N., Huber, D. F., & Hebert, M. (2006) · Journal of Field Robotics · doi:10.1002/rob.20134 · Cited by 463 · see citing works
Unsupervised feature learning for 3D scene labeling
Lai, K., Bo, L., & Fox, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6907298 · Cited by 317 · see citing works
Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments
Kelly, A., Stentz, A., Amidi, O., Bode, M., Bradley, D., Díaz-Calderón, A., Happold, M., Herman, H., MANDELBAUM, R. A., Pilarski, T., et al. (2006) · The International Journal of Robotics Research · doi:10.1177/0278364906065543 · Cited by 239 · see citing works
Lidar-Based Hazard Avoidance for Safe Landing on Mars
Johnson, A., Klumpp, A. R., Collier, J. B., & Wolf, A. A. (2002) · Journal of Guidance Control and Dynamics · doi:10.2514/2.4988 · Cited by 180 · see citing works
Deep Learning for Detecting Robotic Grasps
Lenz, I., Lee, H., & Saxena, A. (2013) · Robotics: Science and Systems IX · doi:10.15607/rss.2013.ix.012 · Cited by 126 · see citing works
Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields
Munoz, D., Vandapel, N., & Hebert, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152856 · Cited by 123 · see citing works
FLIRT - Interest regions for 2D range data
Tipaldi, G. D., & Arras, K. O. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509864 · Cited by 112 · see citing works
Autonomous landing at unprepared sites by a full-scale helicopter
Scherer, S., Chamberlain, L., & Singh, S. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.09.004 · Cited by 104 · see citing works
Self-citationNON-ASSOCIATIVE MARKOV NETWORKS FOR 3D POINT CLOUD CLASSIFICATION
Шаповалов, Р., Velizhev, A., Barinova, O., & Gory, L. (2010) · Cited by 95 · see citing works
A Generative Model of Terrain for Autonomous Navigation in Vegetation
Wellington, C., Courville, A. C., & Stentz, A. (2006) · The International Journal of Robotics Research · doi:10.1177/0278364906072769 · Cited by 73 · see citing works
Ladar-based Discrimination of Grass from Obstacles for Autonomous Navigation
Macedo, J., Manduchi, R., & Matthies, L. H. (2007) · Springer eBooks · doi:10.1007/3-540-45118-8_12 · Cited by 64 · see citing works
A Pipeline for the Segmentation and Classification of 3D Point Clouds
Douillard, B., Underwood, J., Vlaskine, V., Quadros, A., & Singh, S. P. N. (2013) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-28572-1_40 · Cited by 61 · see citing works
Making a Science of Model SearchPreprint
Bergstra, J., Yamins, D. L. K., & Cox, D. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1209.5111 · Cited by 50 · see citing works
Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments
Kelly, A., Amidi, O., Bode, M., Happold, M., Herman, H., Pilarski, T., Rander, P. W., Stentz, A., Vallidis, N., & Warner, R. (2006) · Springer tracts in advanced robotics · doi:10.1007/11552246_57 · Cited by 40 · see citing works
Accurate rough terrain estimation with space-carving kernels
Hadsell, R. T., Bagnell, J. A., Huber, D., & Hebert, M. (2009) · Robotics: Science and Systems · doi:10.15607/rss.2009.v.019 · Cited by 38 · see citing works
Foliage discrimination using a rotating ladar
Castaño, A., & Matthies, L. H. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2003.1241564 · Cited by 32 · see citing works
Enabling Aircraft Emergency Landings Using Active Visual Site Detection
Warren, M., Mejías, L., Yang, X., Arain, B., Gonzalez, F., & Upcroft, B. (2014) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-07488-7_12 · Cited by 31 · see citing works
Field-Testing of a Helicopter UAV Obstacle Field Navigation and Landing System
Whalley, M. S., Takahashi, M. D., Amrdec, nbsp, Tsenkov, P., Schulein, G. J., & Goerzen, C. L. (2009) · AHS International Forum 65 · Cited by 27 · see citing works
A 3D extension to cortex like mechanisms for 3D object class recognition
Flitton, G. T., Breckon, T. P., & Megherbi, N. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248109 · Cited by 20 · see citing works
Automatic data driven vegetation modeling for lidar simulation
Deschaud, J., Prasser, D., Dias, M. F., Brett Browning, A., & Rander, P. W. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225269 · Cited by 16 · see citing works
Scherer, S., Choudhury, S., & Arora, S. (2014) · Vertical Flight Society Forum · doi:10.4050/f-0070-2014-9676 · Cited by 15 · see citing works
Self-citation
Flagged first, then by citations
4 of the 38 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Zhou, Y., & Tuzel, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00472 · Cited by 4,813 · see citing works
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Maturana, D., & Scherer, S. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353481 · Cited by 3,674 · see citing works
Self-citationFoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
Yang, Y. L., Feng, C., Shen, Y., & Tian, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00029 · Cited by 1,365 · see citing works
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
Zeng, A., Song, S., Nießner, M., Fisher, M., Xiao, J., & Funkhouser, T. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.29 · Cited by 1,132 · see citing works
Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8462926 · Cited by 967 · see citing works
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M., & Kautz, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00268 · Cited by 804 · see citing works
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746 · see citing works
Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks
Engelcke, M., Rao, D., Wang, D. Z., Tong, C. H., & Posner, I. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989161 · Cited by 549 · see citing works
Mvx-net: Multimodal voxelnet for 3d object detection
Sindagi, V. A., Zhou, Y., & Tuzel, O. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8794195 · Cited by 554 · see citing works
3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images
Ji, S., Zhang, C., Xu, A., Shi, Y. B., & Duan, Y. (2018) · Remote Sensing · doi:10.3390/rs10010075 · Cited by 397 · see citing works
Review: Deep Learning on 3D Point Clouds
Bello, S. A., Yu, S., Wang, C., Adam, J. M., & Li, J. (2020) · Remote Sensing · doi:10.3390/rs12111729 · Cited by 399 · see citing works
Point cloud labeling using 3D Convolutional Neural Network
Huang, J., & You, S. O. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900038 · Cited by 353 · see citing works
Ge, L., Liang, H., Yuan, J., & Thalmann, D. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.602 · Cited by 290 · see citing works
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels
Sharma, A., Grau, O., & Fritz, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_20 · Cited by 266 · see citing works
Zhou, S., Shen, W., Zeng, D., Fang, M., Wei, Y., & Zhang, Z. (2016) · Signal Processing Image Communication · doi:10.1016/j.image.2016.06.007 · Cited by 258 · see citing works
A Review of Vehicle Detection Techniques for Intelligent Vehicles
Wang, Z., Zhan, J., Duan, C., Guan, X., Lu, P., & Yang, K. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3128968 · Cited by 258 · see citing works
Sparse 3D convolutional neural networks
Graham, B. V. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.150 · Cited by 230 · see citing works
A deep-network solution towards model-less obstacle avoidance
Tai, L., Li, S., & Liu, M. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759428 · Cited by 212 · see citing works
Gavahi, K., Abbaszadeh, P., & Moradkhani, H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115511 · Cited by 173 · see citing works
Recurrent Attention Models for Depth-Based Person Identification
Haque, A., Alahi, A., & Fei-Fei, L. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.138 · Cited by 172 · see citing works
Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning
Kamrava, S., Tahmasebi, P., & Sahimi, M. (2019) · Transport in Porous Media · doi:10.1007/s11242-019-01352-5 · Cited by 165 · see citing works
Active Autonomous Aerial Exploration for Ground Robot Path Planning
Delmerico, J., Mueggler, E., Nitsch, J., & Scaramuzza, D. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2651163 · Cited by 149 · see citing works
Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks
Huang, H., Kalogerakis, E., Chaudhuri, S., Ceylan, D., Kim, V. G., & Yumer, E. (2017) · ACM Transactions on Graphics · doi:10.1145/3137609 · Cited by 139 · see citing works
Towards Viewpoint Invariant 3D Human Pose Estimation
Haque, A., Peng, B., Luo, Z., Alahi, A., Yeung, S., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_10 · Cited by 169 · see citing works
A survey of safe landing zone detection techniques for autonomous unmanned aerial vehicles (UAVs)
Alam, M. S., & Oluoch, J. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115091 · Cited by 131 · see citing works
Liu, F., Li, S., Zhang, L., Zhou, C., Ye, R., Wang, Y., & Lu, J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.605 · Cited by 117 · see citing works
A survey on deep geometry learning: From a representation perspective
Xiao, Y., Lai, Y., Zhang, F., Li, C., & Gao, L. (2020) · Computational Visual Media · doi:10.1007/s41095-020-0174-8 · Cited by 118 · see citing works
Semantics for Robotic Mapping, Perception and Interaction: A Survey
Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I. D., Gould, S. J., et al. (2020) · Foundations and Trends in Robotics · doi:10.1561/2300000059 · Cited by 114 · see citing works
Deep learning-based spacecraft relative navigation methods: A survey
Song, J., Rondao, D., & Aouf, N. (2022) · Acta Astronautica · doi:10.1016/j.actaastro.2021.10.025 · Cited by 106 · see citing works
“In 2015, Maturana and Scherer [78] used what they called a 3D-CNN to create a safety map for autonomous landing zone detection for helicopters.”
MethodThe Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory
Ayrey, E., & Hayes, D. J. (2018) · Remote Sensing · doi:10.3390/rs10040649 · Cited by 106 · see citing works
Jin, S., Su, Y., Gao, S., Wu, F., Ma, Q., Xu, K., Ma, Q., Hu, T., Liu, J., Pang, S., et al. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2953092 · Cited by 106 · see citing works
3D Parallel Fully Convolutional Networks for Real-Time Video Wildfire Smoke Detection
Li, X., Chen, Z., Wu, Q. M. J., & Liu, C. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2889193 · Cited by 100 · see citing works
Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks
Ge, L., Liang, H., Yuan, J., & Thalmann, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2827052 · Cited by 95 · see citing works
Large-scale urban point cloud labeling and reconstruction
Zhang, L., Li, Z., Li, A., & Liu, F. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.02.008 · Cited by 91 · see citing works
Deep part induction from articulated object pairs
Yi, L., Huang, H., Liu, D., Kalogerakis, E., Su, H., & Guibas, L. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275027 · Cited by 81 · see citing works
Urban 3D modeling using mobile laser scanning: a review
Wang, C., Wen, C., Dai, Y., Yu, S., & Liu, M. (2020) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2020.05.003 · Cited by 82 · see citing works
Learning localized features in 3D CAD models for manufacturability analysis of drilled holes
Ghadai, S., Balu, A., Sarkar, S., & Krishnamurthy, A. (2018) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2018.03.024 · Cited by 77 · see citing works
Aerial Image Road Extraction Based on an Improved Generative Adversarial Network
Zhang, X., Han, X., Li, C., Tang, X., Zhou, H., & Jiao, L. (2019) · Remote Sensing · doi:10.3390/rs11080930 · Cited by 77 · see citing works
Zhang, L., & Zhang, L. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2769120 · Cited by 75 · see citing works
HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud Segmentation
Chen, C., Qian, S., Fang, Q., & Xu, C. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3009499 · Cited by 73 · see citing works
Xu, Z., Guan, K., Casler, N., Peng, B., & Wang, S. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.08.005 · Cited by 72 · see citing works
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to ImitationPreprint
Tai, L., Zhang, J., Liu, M., Boedecker, J., & Burgard, W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.07139 · Cited by 95 · see citing works
End-to-End Learning of Keypoint Detector and Descriptor for Pose Invariant 3D Matching
Georgakis, G., Karanam, S., Wu, Z., Ernst, J., & Košecká, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00210 · Cited by 64 · see citing works
Inverse Design of Inflatable Soft Membranes Through Machine Learning
Forte, A. E., Hanakata, P. Z., Jin, L., Zari, E., Zareei, A., Fernandes, M. C., Sumner, L., Alvarez, J. T., & Bertoldi, K. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202111610 · Cited by 62 · see citing works
A Deep Neural Network With Spatial Pooling (DNNSP) for 3-D Point Cloud Classification
Wang, Z., Zhang, L., Zhang, L., Li, R., Zheng, Y., & Zhu, Z. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2829625 · Cited by 61 · see citing works
Learning Semantic Deformation Flows with 3D Convolutional Networks
Yumer, M. E., & Mitra, N. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_18 · Cited by 60 · see citing works
Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not.Preprint
Tai, L., & Liu, M. (2016) · arXiv (Cornell University) · Cited by 59 · see citing works
Mack, J., Lenz, C., Teutrine, J., & Steinhage, V. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.02.017 · Cited by 55 · see citing works
A Deep Learning for Brain Tumor MRI Images Semantic Segmentation Using FCN
Kumar, S., Negi, A., Singh, J., & Verma, H. (2018) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2018.8777675 · Cited by 54 · see citing works
Optimizing N-dimensional, winograd-based convolution for manycore CPUs
Jia, Z., Zlateski, A., Durand, F., & Li, K. (2018) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/3178487.3178496 · Cited by 52 · see citing works
Qin, N., Hu, X., & Dai, H. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.03.011 · Cited by 49 · see citing works
Survey on path and view planning for UAVs
Zhou, X., Yi, Z., Liu, Y., Huang, K., & Huang, H. F. (2020) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2019.12.004 · Cited by 49 · see citing works
Matsuzaka, Y., & Uesawa, Y. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00065 · Cited by 47 · see citing works
Cyber-Physical Attack Threats Analysis for UAVs from CPS Perspective
Rong-xiao, G., Ji-wei, T., Bu-hong, W., & Fu-te, S. (2020) · International Conference on Computer Engineering and Application (ICCEA) · doi:10.1109/iccea50009.2020.00063 · Cited by 44 · see citing works
Label-Efficient Learning on Point Clouds Using Approximate Convex Decompositions
Gadelha, M., RoyChowdhury, A., Sharma, G., Kalogerakis, E., Cao, L., Miller, E. G., Wang, R., & Maji, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58607-2_28 · Cited by 42 · see citing works
Autonomous exploration of mobile robots through deep neural networks
Tai, L., Li, S., & Liu, M. (2017) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881417703571 · Cited by 36 · see citing works
Kantzos, C., Lao, J., & Rollett, A. D. (2019) · Materials Characterization · doi:10.1016/j.matchar.2019.109961 · Cited by 35 · see citing works
Monocular vision with deep neural networks for autonomous mobile robots navigation
Sleaman, W. K., Hameed, A. A., & Jamıl, A. (2022) · Optik · doi:10.1016/j.ijleo.2022.170162 · Cited by 35 · see citing works
Brain Tumor Segmentation and Classification Using MRI Images via Fully Convolution Neural Networks
Kumar, S., Negi, A., Singh, J., & Gaurav, A. (2018) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icacccn.2018.8748614 · Cited by 34 · see citing works
A multiclass TrAdaBoost transfer learning algorithm for the classification of mobile lidar data
He, H., Khoshelham, K., & Fraser, C. S. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.05.010 · Cited by 34 · see citing works
Samaniego, F., Sanchis, J., García-Nieto, S., & Simarro, R. (2019) · Electronics · doi:10.3390/electronics8030306 · Cited by 32 · see citing works
A Deep Learning Method to Accelerate the Disaster Response Process
Antoniou, V., & Potsiou, C. (2020) · Remote Sensing · doi:10.3390/rs12030544 · Cited by 34 · see citing works
LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR
Li, P., Zhao, R., Shi, Y., Zhao, H., Yuan, J., Zhou, G., & Zhang, Y. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160552 · Cited by 32 · see citing works
SurfConv: Bridging 3D and 2D Convolution for RGBD Images
Chu, H., Ma, W.-C., Kundu, K., Urtasun, R., & Fidler, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00317 · Cited by 30 · see citing works
Deep Learning-Based Damage Detection from Aerial SfM Point Clouds
Mohammadi, M. E., Watson, D., & Wood, R. L. (2019) · Drones · doi:10.3390/drones3030068 · Cited by 30 · see citing works
Lee, M.-F. R. (2023) · Processes · doi:10.3390/pr11113171 · Cited by 26 · see citing works
A Safe Landing Site Selection Method of UAVs Based on LiDAR Point Clouds
Yan, L., Qi, J., Wang, M., Wu, C. H., & Xin, J. (2020) · Chinese Control Conference (CCC) · doi:10.23919/ccc50068.2020.9189499 · Cited by 27 · see citing works
A Real-Time and Multi-Sensor-Based Landing Area Recognition System for UAVs
Liu, F., Shan, J., Xiong, B., & Fang, Z. (2022) · Drones · doi:10.3390/drones6050118 · Cited by 27 · see citing works
Kumar, B., Pandey, G., Lohani, B., & Misra, S. C. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.11.006 · Cited by 26 · see citing works
Using three dimensional convolutional neural networks for denoising echosounder point cloud data
Stephens, D., Smith, A., Redfern, T., Talbot, A., Lessnoff, A., & Dempsey, K. (2019) · Applied Computing and Geosciences · doi:10.1016/j.acags.2019.100016 · Cited by 25 · see citing works
Season-Invariant Semantic Segmentation with a Deep Multimodal Network
Kim, D.-K., Maturana, D., Uenoyama, M., & Scherer, S. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-67361-5_17 · Cited by 24 · see citing works
Self-citationMVF-CNN: Fusion of Multilevel Features for Large-Scale Point Cloud Classification
Li, Y., Tong, G., Li, X., Zhang, L., & Peng, H. (2019) · IEEE Access · doi:10.1109/access.2019.2908983 · Cited by 24 · see citing works
García-García, A., García‐Rodríguez, J., Orts‐Escolano, S., Oprea, S., Gomez‐Donoso, F., & Cazorla, M. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.06.006 · Cited by 23 · see citing works
Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles
Rodríguez-Ramos, A., Sampedro, C., Bavle, H., Milošević, Z., Garcia-Vaquero, A., & Campoy, P. (2017) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2017.7991438 · Cited by 23 · see citing works
3DCNN-Based Real-Time Driver Fatigue Behavior Detection in Urban Rail Transit
Liu, Y., Zhang, T., & Li, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2945136 · Cited by 18 · see citing works
Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles
Loureiro, G. S. M., Dias, A. M. P., Martins, A., & Almeida, J. M. (2021) · Remote Sensing · doi:10.3390/rs13101930 · Cited by 21 · see citing works
FFPointNet: Local and global fused feature for 3D point clouds analysis
Bello, S. A., Wang, C., Wambugu, N. M., & Adam, J. M. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.044 · Cited by 21 · see citing works
RGB-D Image-Based Object Detection: From Traditional Methods to Deep Learning Techniques
Ward, I. R., Laga, H., & Bennamoun, M. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-28603-3_8 · Cited by 21 · see citing works
Structure-functional analysis and synthesis of deep convolutional neural networks
Vizilter, Y. V., Сергеевич, Г. В., & Zheltov, S. Y. (2019) · Computer Optics · doi:10.18287/2412-6179-2019-43-5-886-900 · Cited by 20 · see citing works
Ayrey, E., Hayes, D. J., Kilbride, J. B., Fraver, S., Kershaw, J. A., Cook, B. D., & Weiskittel, A. R. (2021) · Remote Sensing · doi:10.3390/rs13245113 · Cited by 20 · see citing works
3D Scene Reconstruction with Sparse LiDAR Data and Monocular Image in Single Frame
Zhong, Y., Wang, S., Xie, S., Cao, Z. L., Jiang, K., & Yang, D. (2017) · SAE International journal of passenger cars. Electronic and electrical systems · doi:10.4271/07-11-01-0005 · Cited by 20 · see citing works
Joint Discriminative Dictionary and Classifier Learning for ALS Point Cloud Classification
Zhang, Z., Zhang, L., Tan, Y., Zhang, L., Liu, F., & Zhong, R. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2751061 · Cited by 20 · see citing works
Deep and Sparse features For Anomaly Detection and Localization in video
Sabzalian, B., Marvi, H., & Ahmadyfard, A. (2019) · doi:10.1109/pria.2019.8786007 · Cited by 20 · see citing works
Depth-Adapted CNN for RGB-D Cameras
Wu, Z., Allibert, G., Stolz, C., & Demonceaux, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_24 · Cited by 19 · see citing works
Autonomous UAVs landing site selection from point cloud in unknown environments
Yang, L., Wang, C., & Wang, L. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.04.005 · Cited by 19 · see citing works
Murray, X., Apan, A., Deo, R. C., & Maraseni, T. (2022) · Geocarto International · doi:10.1080/10106049.2022.2048902 · Cited by 18 · see citing works
An auto-encoder based classifier for crop mapping from multitemporal multispectral imagery
Hamidi, M., Safari, A., & Homayouni, S. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1820619 · Cited by 17 · see citing works
Reinforcement learning for shared autonomy drone landings
Backman, K., Kulić, D., & Chung, H. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10143-3 · Cited by 17 · see citing works
Landing sites detection using LiDAR data on manycore systems
Lorenzo, Ó. G., Sánchez, J. M., Vilariño, D. L., Pena, T. F., Cabaleiro, J. C., & Rivera, F. F. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-016-1912-7 · Cited by 17 · see citing works
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen CategoriesPreprint
Luo, T., Mo, K., Huang, Z., Xu, J., Hu, S., Wang, L., & Su, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.06478 · Cited by 19 · see citing works
Mobile robot monocular vision-based obstacle avoidance algorithm using a deep neural network
Rezaei, N., & Darabi, S. (2023) · Evolutionary Intelligence · doi:10.1007/s12065-023-00829-z · Cited by 16 · see citing works
Detection and Tracking of Small Objects in Sparse 3D Laser Range Data
Razlaw, J., Quenzel, J., & Behnke, S. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794204 · Cited by 16 · see citing works
Huk, M. (2016) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169134 · Cited by 16 · see citing works
DAPnet: A Double Self-Attention Convolutional Network for Point Cloud Semantic Labeling
Chen, L., Chen, W., Xu, Z., Huang, H., Wang, S., Zhu, Q., & Li, H. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3113047 · Cited by 15 · see citing works
3D Deep Learning for Biological Function Prediction from Physical Fields
Golkov, V., Skwark, M. J., Mirchev, A., Dikov, G., Geanes, A. R., Mendenhall, J., Meiler, J., & Cremers, D. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00103 · Cited by 15 · see citing works
Integrated Emergency Self-Landing Method for Autonomous UAS in Urban Aerial Mobility
Xia, B., Mantegh, I., & Xie, W. (2021) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas52745.2021.9649955 · Cited by 14 · see citing works
Off-Road Lidar Simulation with Data-Driven Terrain Primitives
Tallavajhula, A., Mericli, U., & Kelly, A. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461198 · Cited by 14 · see citing works
Self‐Supervised Learning of Part Mobility from Point Cloud Sequence
Shi, Y., Cao, X., & Zhou, B. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14207 · Cited by 13 · see citing works
Yamao, S., Hidaka, H., Odashima, S., Jiang, S., & Murase, Y. (2017) · IEEE International Conference on Advanced Intelligent Mechatronics (AIM) · doi:10.1109/aim.2017.8014014 · Cited by 12 · see citing works
Point Cloud Processing via Recurrent Set Encoding
Wu, P., Chen, C., Yi, J., & Metaxas, D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015441 · Cited by 13 · see citing works
- Authors
- Maturana, D., & Scherer, S.
- Affiliations
- Carnegie Mellon University (US)
- ORCID
- Scherer, Sebastian
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 3471-3478
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1493004075
- Cited by
- 203
- References
- 38
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 7 · 2023: 12 · 2022: 15 · 2021: 20 · 2020: 24 · 2019: 34 · 2018: 33 · 2017: 27 · 2016: 18 · 2015: 3
- FWCI
- 11.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Funders
- Office of Naval Research
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Environmental Science › Environmental Engineering › Remote Sensing and LiDAR Applications
- Keywords
- LiDAR point cloud, 3D convolutional neural networks, obstacle detection, vegetated terrain, autonomous helicopter
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