ICRA2018点云相关论文汇总

1.Incremental Segment-Based Localization in 3D Point Clouds

• We propose an efficient method for localization based on 3D segment matching.
• A set of incremental algorithms for the normal estimation, segmentation and recognition steps is presented.
• Localization at 10Hz in urban driving environment is achieved (speedup of x7.1 over batch solution).
• The implementation is available open source:

 

2.SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

• LiDAR point cloud segmentation is important for autonomous driving.
• We propose a CNN based model with high accuracy and real-time inference speed.
• Real-world data and simulated data are combined to train the model.
• Source code is released: https://github.com/BichenWuUCB/SqueezeSeg

3.Sampled-Point Network for Classification of Deformed Building Element Point Clouds</