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.

翻译：本文提出了一种用于自动驾驶或自动化车辆及/或机器人应用传感器配置的盲区估计方法。与先前依赖几何近似的方案相比，本方法通过利用精确详尽的3D仿真环境，提供了更符合实际的覆盖范围评估。该方法利用目标场景高保真仿真中激光雷达传感器的点云或相机深度图像，提供精准且可执行的能见度估计。基于蒙特卡洛的参考传感器仿真使我们能够准确估计作为覆盖度指标的盲区尺寸，以及任意位置目标的检测概率。