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：本文提出一種用於自動駕駛/自動化車輛及機器人應用中感測器配置盲區的估計方法。相較於以往依賴幾何近似的方法，本方法透過精確且詳盡的三維模擬環境，提供更貼近真實的覆蓋範圍估計。我們利用目標情境高保真模擬中產生的雷射雷達點雲或相機深度影像，提供準確且可操作的能見度評估。基於蒙地卡羅的參考感測器模擬技術，不僅能精確估算作為覆蓋率指標的盲區大小，還能計算任意位置物體的偵測機率。