A Sensor Simulation Framework for Training and Testing Robots and Autonomous Vehicles
A Sensor Simulation Framework for Training and Testing Robots and Autonomous Vehicles
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DOI：
10.1115/1.4050080
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发表时间：
2021-04
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影响因子：
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通讯作者：
A. Elmquist;R. Serban;D. Negrut
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作者：
A. Elmquist;R. Serban;D. Negrut
计算机仿真可以是一个有用的工具时，设计机器人预计独立操作的非结构化环境。在这种情况下，需要模拟机器人的机械系统的动力学，机器人操作的环境，以及促进机器人感知环境的传感器。在这里，我们专注于传感模拟任务，提出了一个虚拟传感框架，与一个开源的多物理仿真平台Chrono一起构建。该框架支持相机、激光雷达、GPS和IMU仿真。我们讨论了它们的建模以及为增加合成传感器数据的真实性而实施的噪声和失真。我们以两个例子结束，这两个例子显示了工作中的传感模拟框架：一个涉及到缩小规模的自动驾驶汽车，第二个涉及到在麦迪逊社区的数字复制品中驾驶的车辆。
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.