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Point Cloud based Hierarchical Deep Odometry Estimation

arXiv 2103.03394 ↗First posted 5 Mar 2021cs.CV cs.CG cs.ROcited by 0

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Abstract (arXiv)

Processing point clouds using deep neural networks is still a challenging task. Most existing models focus on object detection and registration with deep neural networks using point clouds. In this paper, we propose a deep model that learns to estimate odometry in driving scenarios using point cloud data. The proposed model consumes raw point clouds in order to extract frame-to-frame odometry estimation through a hierarchical model architecture. Also, a local bundle adjustment variation of this model using LSTM layers is implemented. These two approaches are comprehensively evaluated and are compared against the state-of-the-art.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 5 Mar 2021.

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20 of 38 references.

References · 20

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
  9. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 784
  10. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  11. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 657
  12. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 579
8 more
  1. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  2. 2018FlowNet3D: Learning Scene Flow in 3D Point Cloudscited by 242
  3. 2017DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networkscited by 175
  4. 2017Geometry-Aware Learning of Maps for Camera Localizationcited by 161
  5. 2017Deep Learning Features at Scale for Visual Place Recognitioncited by 96
  6. 2019LO-Net: Deep Real-time Lidar Odometrycited by 60
  7. 2019SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventorycited by 17
  8. 2019DeepLO: Geometry-Aware Deep LiDAR Odometrycited by 9

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