Mr. Charlie M. Kershner Profile

Charlie M. Kershner

SPIE Involvement:

Conference Program Committee | Author

SPIE Journal Paper | 10 August 2024 Open Access

Toward standardized testing of automotive lidars: year two results

Theus Aspiras, Vijayan Asari, Cullen Bradley, Anna Gnacek, Charlie Kershner, Daniel LeMaster, Paul McManamon, Andrew Reinhardt, Eddie Ruff, Christopher Valenta, Andre Van Rynbach

OE, Vol. 63, Issue 08, 085102, (August 2024) https://doi.org/10.1117/12.10.1117/1.OE.63.8.085102

KEYWORDS: LIDAR, Fiber optic gyroscopes, Standards development, Roads, Reflectivity, Laser safety, Safety, Optical engineering, Target detection, Point clouds

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SPIE Journal Paper | 5 January 2023 Open Access

Toward open benchmark tests for automotive lidars, year 1: static range error, accuracy, and precision

Zach Jeffries, Jeremy Bos, Paul McManamon, Charles Kershner, Akhil Kurup

OE, Vol. 62, Issue 03, 031211, (January 2023) https://doi.org/10.1117/12.10.1117/1.OE.62.3.031211

KEYWORDS: LIDAR, Target detection, Rain, Optical engineering, Standards development, Point clouds, Fiber optic gyroscopes, Manufacturing, Calibration, Design and modelling

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Proceedings Article | 20 June 2014 Paper

Invariant-feature-based adaptive automatic target recognition in obscured 3D point clouds

Timothy Khuon, Charles Kershner, Enrico Mattei, Arnel Alverio, Robert Rand

Proceedings Volume 9091, 90911F (2014) https://doi.org/10.1117/12.2064801

KEYWORDS: Clouds, Neural networks, Sensors, Feature extraction, LIDAR, Detection and tracking algorithms, 3D acquisition, Automatic target recognition, Super resolution, Neurons

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Target recognition and classification in a 3D point cloud is a non-trivial process due to the nature of the data collected from a sensor system. The signal can be corrupted by noise from the environment, electronic system, A/D converter, etc. Therefore, an adaptive system with a desired tolerance is required to perform classification and recognition optimally. The feature-based pattern recognition algorithm architecture as described below is particularly devised for solving a single-sensor classification non-parametrically. Feature set is extracted from an input point cloud, normalized, and classifier a neural network classifier. For instance, automatic target recognition in an urban area would require different feature sets from one in a dense foliage area. The figure above (see manuscript) illustrates the architecture of the feature based adaptive signature extraction of 3D point cloud including LIDAR, RADAR, and electro-optical data. This network takes a 3D cluster and classifies it into a specific class. The algorithm is a supervised and adaptive classifier with two modes: the training mode and the performing mode. For the training mode, a number of novel patterns are selected from actual or artificial data. A particular 3D cluster is input to the network as shown above for the decision class output. The network consists of three sequential functional modules. The first module is for feature extraction that extracts the input cluster into a set of singular value features or feature vector. Then the feature vector is input into the feature normalization module to normalize and balance it before being fed to the neural net classifier for the classification. The neural net can be trained by actual or artificial novel data until each trained output reaches the declared output within the defined tolerance. In case new novel data is added after the neural net has been learned, the training is then resumed until the neural net has incrementally learned with the new novel data. The associative memory capability of the neural net enables the incremental learning. The back propagation algorithm or support vector machine can be utilized for the classification and recognition.

Conference Committee Involvement (1)

Laser Radar Technology and Applications XXX

16 April 2025 | Orlando, Florida, United States

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