Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. However, they are known to be sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR). As a result, lidar-based object detectors trained on data captured in normal weather tend to perform poorly in such scenarios. However, collecting and labelling sufficient training data in a diverse range of adverse weather conditions is laborious and prohibitively expensive. To address this issue, we propose a physics-based approach to simulate lidar point clouds of scenes in adverse weather conditions. These augmented datasets can then be used to train lidar-based detectors to improve their all-weather reliability. Specifically, we introduce a hybrid Monte-Carlo based approach that treats (i) the effects of large particles by placing them randomly and comparing their back reflected power against the target, and (ii) attenuation effects on average through calculation of scattering efficiencies from the Mie theory and particle size distributions. Retraining networks with this augmented data improves mean average precision evaluated on real world rainy scenes and we observe greater improvement in performance with our model relative to existing models from the literature. Furthermore, we evaluate recent state-of-the-art detectors on the simulated weather conditions and present an in-depth analysis of their performance.
翻译:以利达尔为基础的天体探测器是自动驾驶汽车等自主导航系统3D感知管道的关键部分,但众所周知,由于信号对噪音比(SNR)和信号对背地面比(SBR)的降低,这些天体探测器对雨水、雪和雾等恶劣天气条件敏感,因此,在正常天气中采集的数据方面受过训练的利达尔天体探测器往往在正常天气中表现不佳。然而,在各种不利天气条件下收集和标记足够的培训数据是艰苦和昂贵的。为解决这一问题,我们建议采用基于物理的办法来模拟不利天气条件下的场景的利达尔点云。这些扩大的数据集可用于训练以利达尔为基础的探测器,以提高其全天候可靠性。具体地说,我们采用基于蒙特卡洛的混合天体探测器方法来处理(一)大型粒子在随机排列和比较其反射力相对于目标的影响,以及(二)通过计算米氏理论和粒子大小分布的散射效率来降低平均影响。我们用这种增强的物理质量的方法,对最新的天气模型进行再培训网络,从目前的更精确性分析,我们用这种不断增强的模型来评估。