论文标题
部分可观测时空混沌系统的无模型预测
RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments
论文作者
论文摘要
相机重新定位在自动驾驶中具有各种应用。以前的相机姿势回归模型仅考虑几乎没有环境扰动的理想场景。为了应对可能有变化的季节，天气，照明和不稳定物体的挑战性驾驶环境，我们提出了Robustloc，该驾驶环境可鲁ustloc，它具有对神经微分方程的扰动的稳健性。我们的模型使用卷积神经网络从多视图图像，强大的神经微分方程扩散块模块中提取特征图，以交互性地扩散信息，以及具有多层训练的分支姿势解码器，以估计车辆姿势。实验表明，Rubustloc超过了当前的最新摄像头姿势回归模型，并在各种环境中实现了稳健的性能。我们的代码在以下网址发布：https：//github.com/sijieaaa/robustloc
Camera relocalization has various applications in autonomous driving. Previous camera pose regression models consider only ideal scenarios where there is little environmental perturbation. To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable objects, we propose RobustLoc, which derives its robustness against perturbations from neural differential equations. Our model uses a convolutional neural network to extract feature maps from multi-view images, a robust neural differential equation diffusion block module to diffuse information interactively, and a branched pose decoder with multi-layer training to estimate the vehicle poses. Experiments demonstrate that RobustLoc surpasses current state-of-the-art camera pose regression models and achieves robust performance in various environments. Our code is released at: https://github.com/sijieaaa/RobustLoc
