Abstract-The detection of features from Light Detection and Ranging (LIDAR) data is a fundamental component of featurebased mapping and SLAM systems. Classical approaches are often tied to specific environments, computationally expensive, or do not extract precise features. We describe a general purpose feature detector that is not only efficient, but also applicable to virtually any environment. Our method shares its mathematical foundation with feature detectors from the computer vision community, where structure tensor based methods have been successful. Our resulting method is capable of identifying stable and repeatable features at a variety of spatial scales, and produces uncertainty estimates for use in a state estimation algorithm. We verify the proposed method on standard datasets, including the Victoria Park dataset and the Intel Research Center dataset.
Li et al. REF also published a general purpose feature detector using structure tensors of surface normals.
Structure tensors for general purpose LIDAR feature extraction
2011 IEEE International Conference on Robotics and Automation
2011 IEEE International Conference on Robotics and Automation
Engineering Computer Science