Conference Paper

A probabilistic representation of LiDAR range data for efficient 3D object detection

Dept. of Electr., Comput., & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY
DOI: 10.1109/CVPRW.2008.4563033 Conference: Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on
Source: IEEE Xplore


We present a novel approach to 3D object detection in scenes scanned by LiDAR sensors, based on a probabilistic representation of free, occupied, and hidden space that extends the concept of occupancy grids from robot mapping algorithms. This scene representation naturally handles LiDAR sampling issues, can be used to fuse multiple LiDAR data sets, and captures the inherent uncertainty of the data due to occlusions and clutter. Using this model, we formulate a hypothesis testing methodology to determine the probability that given 3D objects are present in the scene. By propagating uncertainty in the original sample points, we are able to measure confidence in the detection results in a principled way. We demonstrate the approach in examples of detecting objects that are partially occluded by scene clutter such as camouflage netting.

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    • "Our work is related to others. Among them, let us quote Yapo et al. [6] who proposed a method to detect 3D objects using LIDAR sensors. Their approach is also based on the concept of occupancy grids. "

    Full-text · Article · Oct 2012
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    • "Recently, there has been some investigation into object detection and recognition using 3D LIDAR data, mostly for stationary objects like buildings and trees. Some examples include object recognition in 3D LIDAR data with recurrent neural network [1], coarse-to-fine scheme for object indexing/ verification and rotationally invariant semi-local spin image features for 3D object representation [2], probabilistic representation of LIDAR range data for efficient 3D object detection [3], 3D object recognition from range images using local feature histograms [4], and DARPA URGENT object recognition based on strip-cueing [6]. However, to the best of our knowledge, little work has been done for detection and recognition of moving objects (i.e., persons and vehicles) in motion 3D data. "
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