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
翻译:相机重新定位在自动驱动中有各种应用。 以前的相机显示回归模型只考虑没有多少环境扰动的理想情景。 为了应对具有挑战性的驱动环境, 这些环境可能具有变化的季节、 天气、 光化和不稳定物体的存在, 我们提议 RobustLoc, 它具有抵御神经差异方程式扰动的强力。 我们的模型使用一个脉冲神经网络从多视图图像中提取特征地图, 一个强大的神经差异方程式扩散模块, 以交互方式传播信息, 以及一个配有多层训练的分形解码器, 以估计车辆配置。 实验显示 RobustLoc 超越了当前最先进的相机, 形成回归模型, 并在各种环境中实现强力性运行。 我们的代码发布在 https://github. com/sijieaa/ RobustLoc 上 。