3D Convolutional Neural Networks for landing zone detection from LiDAR

By Daniel Maturana, Sebastian A. Scherer. 2015

217 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition, 3D Vision & Pose

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Citations in the corpus

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017, methodology): “Some of the earlier methods for feature representation are [41, 8, 7, 19, 42, 35, 6, 27, 1, 36, 2, 25, 26].”
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “D DNN Benchmark X Object Detection Grasping FF F Redmon and Angelova (2015) RGBD CNN Benchmark X Object Detection Grasping F FF Li et al. (2016) 3D Lidar CNN Benchmark Object Detection Auto Drive F F Maturana and Scherer (2015a) Lidar 3D Conv Self-Labeled X Classiﬁcation Aerial FF FF Engelcke et al.”
Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks (2016, background): “Dense 3D occupancy grids obtained from point clouds are processed with CNNs in [8] and [9].”
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels (2016, background): “Following the success of Shapenet, there have been recent work [10]that further improves the recognition results on 3D data.”
Sparse 3D convolutional neural networks (2015, methodology): “Three dimensional CNNs have been used to analyze movement in 2+1 dimensional space-time[5, 6] and for helping drones find a safe place to land [12].”
Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, background): “Maturana and Scherer (2015a) applied 3D convolutional neural networks to detect the landing zone for aerial robots.”
Recurrent Attention Models for Depth-Based Person Identification (2016, background): “Spatial volumes and higher-dimensional tensors have been proposed for activity and action recognition [58, 67, 75, 8, 32, 39], medical image analysis [62], robotics [47, 48], and human motion analysis [38] but have not been thoroughly explored in the person identification domain.”
Learning Semantic Deformation Flows with 3D Convolutional Networks (2016, background): “Maturana and Scherer demonstrated the use of 3D convolutional networks in object classification of point clouds [30] and landing zone detection [29], specifically from range sensor data.”
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud (2017, methodology): “Regardless of data preparation, deep learning methods consider endto-end models that leverage 2D convolutional [17] or 3D convolutional [18] neural networks.”
FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds (2017, background): “Applications of learning on point clouds include shape completion and recognition [54], unmanned autonomous vehicles [34], 3D object detection, recognition and classification [9, 31, 38, 39, 45, 46, 50], contour detection [20], layout inference [16], scene labeling [29], category discovery [57], poi”
3DMatch: Learning the Matching of Local 3D Geometry in Range Scans (2016, background): “Additionally, several recent works [21, 11] also extract deep learning features from 3D data for the task of object retrieval and classification for CAD models.”
SPLATNet: Sparse Lattice Networks for Point Cloud Processing (2018, background): “Early voxel-based architectures executed convolution in regular, fixed voxel grids, and were limited to low shape resolutions due to high memory and computation costs [43, 28, 32, 6, 15, 37].”
Point cloud labeling using 3D Convolutional Neural Network (2016, methodology): “For 3D point cloud, Maturana and Scherer applied 3D-CNN for landing zone detection from LiDAR point clouds [8].”
A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles (2017, background): “Regarding laser technology, in [66], a novel strategy for detecting safe landing areas based on the point clouds captured from a LIDAR sensor mounted on a helicopter was proposed.”
Towards Viewpoint Invariant 3D Human Pose Estimation (2016, background): “Other features such as viewpoint invariant 3D feature maps [43], histograms of 3D joint locations [63], multifractal spectrum [64], volumetric attention models [28], and volumetric convolutional filters [45,46] have been proposed for 3D modeling.”
MVX-Net: Multimodal VoxelNet for 3D Object Detection (2019, methodology): “set of techniques represent 3D point cloud data in a voxel grid [24], [23] and employ 3D CNNs to generate detection results.”
3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images (2018, background): “Recently, some studies have utilized 3D CNN for learning spatio-temporal features from videos [29,30], learning 3D structures from LiDAR point clouds [31], or learning spatio-spectral presentations from hyperspectral images [32].”
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation (2017, background): “Applications of learning on point clouds include shape completion and recognition [57], unmanned autonomous vehicles [36], 3D object detection, recognition and classification [9, 33, 40, 41, 48, 49, 53], contour detection [21], layout inference [18], scene labeling [31], category discovery [60], poi”