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Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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LiMOX-A Point Cloud Lidar Model Toolbox Based on NVIDIA OptiX Ray Tracing Engine.

Relindis Rott1, David J Ritter1, Stefan Ladstätter2

  • 1Virtual Vehicle Research GbmH, 8010 Graz, Austria.

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|March 28, 2024
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Summary
This summary is machine-generated.

This study introduces a versatile lidar sensor model for autonomous driving simulation. The ray-tracing model accurately simulates lidar performance, considering material properties and sensor parameters for realistic virtual testing.

Keywords:
infrared material databaselidar sensor modelmodular software architectureray tracingvirtual testing

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Area of Science:

  • Autonomous Systems
  • Robotics
  • Computer Vision

Background:

  • Virtual testing is crucial for autonomous system development, especially for autonomous driving.
  • Perception sensor models are vital for realistic simulation across the sense-plan-act cycle.
  • Existing lidar sensor models in software tools vary in capability.

Purpose of the Study:

  • To present a novel, highly parametrizable point cloud lidar sensor model.
  • To enable simulation of diverse lidar sensors within a modular software architecture.
  • To incorporate physical sensor effects through ray tracing and a material database.

Main Methods:

  • Developed a point cloud lidar sensor model utilizing ray tracing.
  • Integrated an infrared material database to simulate ray-surface interactions.
  • Designed a modular software architecture for standalone usability.
  • Studied angular dependence and maximum range for Lambertian materials.

Main Results:

  • The model accurately simulates lidar sensor effects based on material reflectivity.
  • Maximum detectable range is influenced by material properties.
  • Angular dependence and range variations were analyzed for different target materials.
  • Point clouds were generated and compared for an urban street scene with varying sensor parameters.

Conclusions:

  • The developed lidar sensor model provides a flexible toolbox for simulating various lidar types.
  • It enhances the realism of virtual testing for autonomous driving systems.
  • The approach effectively models physical sensor effects crucial for perception validation.