Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions
- Kansas City National Security Campus (KCNSC), Kansas City, MO (United States)
In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolution of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.
- Research Organization:
- Kansas City Nuclear Security Campus (KCNSC), Kansas City, MO (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- DE-NA0002839
- OSTI ID:
- 2283500
- Report Number(s):
- NSC-614-5849
- Resource Relation:
- Conference: IEEE Sensors 2024, Institute of Electrical and Electronics Engineers, October 20 - 23, 2024, Kobe, Japan
- Country of Publication:
- United States
- Language:
- English
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