A Sensor Simulation Framework for Training and Testing Robots and Autonomous Vehicles
Author(s) -
Asher Elmquist,
Radu Serban,
Dan Negruţ
Publication year - 2021
Publication title -
journal of autonomous vehicles and systems
Language(s) - English
Resource type - Journals
eISSN - 2690-7038
pISSN - 2690-702X
DOI - 10.1115/1.4050080
Subject(s) - robot , computer science , context (archaeology) , focus (optics) , noise (video) , simulation , task (project management) , inertial measurement unit , human–computer interaction , artificial intelligence , systems engineering , engineering , optics , image (mathematics) , biology , paleontology , physics
Computer simulation can be a useful tool when designing robots expected to operate independently in unstructured environments. In this context, one needs to simulate the dynamics of the robot’s mechanical system, the environment in which the robot operates, and the sensors which facilitate the robot’s perception of the environment. Herein, we focus on the sensing simulation task by presenting a virtual sensing framework built alongside an open-source, multi-physics simulation platform called Chrono. This framework supports camera, lidar, GPS, and IMU simulation. We discuss their modeling as well as the noise and distortion implemented to increase the realism of the synthetic sensor data. We close with two examples that show the sensing simulation framework at work: one pertains to a reduced scale autonomous vehicle and the second is related to a vehicle driven in a digital replica of a Madison neighborhood.
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