In this paper, we introduce a method for estimating blind spots for sensor
setups of autonomous or automated vehicles and/or robotics applications. In
comparison to previous methods that rely on geometric approximations, our
presented approach provides more realistic coverage estimates by utilizing
accurate and detailed 3D simulation environments. Our method leverages point
clouds from LiDAR sensors or camera depth images from high-fidelity simulations
of target scenarios to provide accurate and actionable visibility estimates. A
Monte Carlo-based reference sensor simulation enables us to accurately estimate
blind spot size as a metric of coverage, as well as detection probabilities of
objects at arbitrary positions.

本论文介绍了一种用于估计自动驾驶车辆和 / 或机器人应用的传感器设置的盲区的方法。与依赖几何近似的先前方法相比，我们提出的方法利用准确且详细的 3D 模拟环境，提供了更实际的覆盖范围估计。我们的方法利用来自 LiDAR 传感器的点云或高保真仿真目标场景的相机深度图像，提供准确且可操作的可见性估计。基于蒙特卡洛的参考传感器仿真使我们能够准确地估计盲区大小作为覆盖度度量，并了解任意位置上的物体检测概率。