Alibaba Innovative Research (AIR) > Machine Learning (algorithm)
Deep Learning-Based High-Precision LiDAR Localization for Autonomous Driving


Machine Learning (algorithm)


Deep learning-Based High-Precision LiDAR Localization for Autonomous Driving


High-precision Localization, as one of the core technologies of autonomous driving, provides accurate and reliable self-driving positions for autonomous vehicles, which is the basis of autonomous driving. A typical solution is to use a lidar to scan the surrounding environment to obtain structured 3D geometric feature information, and match with the pre-built map to obtain centimeter-level positioning accuracy. In order to obtain stable 3D geometric features, a large amount of parameter tuning and adaptation work is usually required when processing various application scenarios. Although it can achieve excellent performance in most cases, this rule-based approach lacks the general capabilities of business batch replication. For large-scale challenging and unknown application scenarios, a new general framework for LiDAR localization is needed. Recently, there has been research on high-precision localization based on point cloud deep learning. Although in the experimental stage, this large-scale data-driven approach provides the possibility to solve the generalization problem. Moreover, in the process of autonomous driving, a large amount of data will be generated for deep learning training, which can greatly reduce the cost of data collection.


  • The deep neural network is used to extract the multi-dimensional features of the laser point cloud for localization, including geometry, semantic, embedding and other information.
  • Point cloud registration is achieved through end-to-end deep neural network to improve matching accuracy.
  • Combining the point cloud deep neural network model to compress the map, thereby reducing the on-board storage required by the map and reducing processing latency.

Related Research Topics

  • Fast Classification and Segmentation of Point cloud.
  • End-to-End Deep Neural Network for Point Cloud Registration.
  • Robust LiDAR-based place descriptor.
  • Data-Driven Descriptors.
  • Point cloud Compression.
  • Learning to Localize Using a LiDAR intensity/elevation Map.
  • Learning to Localize through Compressed Map.
  • Long-term LiDAR Localization.


Suggested Collaboration Method

AIR (Alibaba Innovative Research), one-year collaboration project. 


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