Abstract
Most commercial uses of LiDAR prefer high-end LiDAR systems with equally sophisticated software packages that have limited accessibility due to cost or complexity. Our purpose for developing this LiDAR point classification framework is to develop a flexible method for visualizing and processing LiDAR data that is simple and cost-effective, and yet achieves a similar degree of functionality as that of its high-end peers. To that end, we have classified data points from a LiDAR-Lite V2 (Blue Label) to represent the distance and height of obstacles, and the gaps between them. This will enable an autonomous mobile agent to determine palatable paths through the satisfactory gaps.
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Phukan, A., Phukan, P., Sinha, R., Hazarika, S., Boruah, A. (2020). Information Encoding, Gap Detection and Analysis from 2D LiDAR Data on Android Environment. In: Pati, B., Panigrahi, C., Buyya, R., Li, KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1082. Springer, Singapore. https://doi.org/10.1007/978-981-15-1081-6_45
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