For information about the pychrono.sensor module including reasons/motives leveraged in the interfac

Chrono Sensor Module

Current Tested Systems:

• Arch Linux:

• Ubuntu 20.04: GCC 9.3, CUDA 10.2

• Windows 10: VS 2019, CUDA 10.2

Supported Sensors

• RGB Mono Camera

• Lidar

• GPS

• IMU

Dependencies

• NVIDIA GPU (required)

  • tested on Maxwell and later

• OptiX (required)

  • 6.5.0

• CUDA (required)

  • tested with CUDA 10.2

• GLFW >= 3.0 (required)

• GLEW >= 1.0 (required)

• openGL (required)

• TensorRT (optional)

  • tested with TensorRT 7.0.0

  • need to explicitly enable TensorRT in cmake by setting CH_USE_TENSOR_RT=ON (default: USE_TENSOR_RT=OFF)

CMake and Build Notes

• consult the Chrono documentation for build instructions.

Getting started with the demos

• consult the Chrono documentation and reference manual for information on the Chrono::Sensor demos.

Current Capabilities

• Scene Rendering

  • lights

    • simple point light

    • shadows

  • Materials

    • reflection based on material reflectance

    • fresnel effect

    • mesh support based on Wavefront OBJ+MTL format

    • programmatic material creation

    • partial transparency without refractance

  • Objects

    • Box primitives

    • Sphere primitives

    • cylinder primitives

    • Triangle Mesh

• Camera sensor

  • ground-truth ray-traced camera rendering

  • filter-based sensor model for user defined sensor model

• Filters

  • Greyscale kernel

  • visualization using GLFW

  • copy-back filter for data access from CPU

  • save images to file at a specific path

  • convert lidar measurements to point cloud

  • image augmentation with pretrained neural nets

• Lidar Sensor

  • single ray and multiray data generation

• GPS Sensor

• IMU Sensor

• Accelerometer and Gyroscope

Capabilities in Progress

• expanded TensorRT model parsing

• development of image augmentation networks

• extending render support (lights, materials, objects, etc)

• expanding mesh file support