Camera localization, i.e., camera pose regression, represents an important task in computer vision since it has many practical applications such as in the context of intelligent vehicles and their localization. Having reliable estimates of the regression uncertainties is also important, as it would allow us to catch dangerous localization failures. In the literature, uncertainty estimation in Deep Neural Networks (DNNs) is often performed through sampling methods, such as Monte Carlo Dropout (MCD) and Deep Ensemble (DE), at the expense of undesirable execution time or an increase in hardware resources. In this work, we considered an uncertainty estimation approach named Deep Evidential Regression (DER) that avoids any sampling technique, providing direct uncertainty estimates. Our goal is to provide a systematic approach to intercept localization failures of camera localization systems based on DNNs architectures, by analyzing the generated uncertainties. We propose to exploit CMRNet, a DNN approach for multi-modal image to LiDAR map registration, by modifying its internal configuration to allow for extensive experimental activity on the KITTI dataset. The experimental section highlights CMRNet's major flaws and proves that our proposal does not compromise the original localization performances but also provides, at the same time, the necessary introspection measures that would allow end-users to act accordingly.

翻译：相机定位，即相机姿态回归，是计算机视觉领域的一项重要任务，在智能车辆及其定位等实际场景中具有广泛应用。对回归不确定性进行可靠估计同样至关重要，因为这能够帮助我们捕捉危险的定位故障。在现有文献中，深度神经网络的不确定性估计通常通过采样方法实现，例如蒙特卡洛丢弃法和深度集成法，但这会带来不可取的执行时间增加或硬件资源消耗。本研究考虑了一种名为深度证据回归的不确定性估计方法，该方法无需任何采样技术即可直接提供不确定性估计。我们的目标是通过分析生成的不确定性，提供一种系统化的方法来拦截基于深度神经网络架构的相机定位系统的定位故障。我们提出利用CMRNet（一种用于多模态图像与激光雷达地图配准的深度神经网络方法），通过修改其内部配置，在KITTI数据集上进行广泛的实验活动。实验部分揭示了CMRNet的主要缺陷，并证明我们的方法既不会牺牲原有的定位性能，又能同时提供必要的自省度量，使最终用户能够据此采取相应行动。