We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. In contrast to other methods that completely replace a classical visual estimator with a deep network, we propose an approach that uses a convolutional neural network to learn difficult-to-model corrections to the estimator from ground-truth training data. To this end, we derive a novel loss function for learning SE(3) corrections based on a matrix Lie groups approach, with a natural formulation for balancing translation and rotation errors. We use this loss to train a Deep Pose Correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment. Using the KITTI odometry dataset, we demonstrate significant improvements to the accuracy of a computationally-efficient sparse stereo visual odometry pipeline, that render it as accurate as a modern computationally-intensive dense estimator. Further, we show how DPC-Net can be used to mitigate the effect of poorly calibrated lens distortion parameters.
翻译:我们提出了一个新颖的方法,将深层网络的力量与几何和概率本地化算法的计算效率结合起来。与其他完全用深网络取代古典视觉测算器的方法不同,我们提出一种方法,用远网络完全取代古典视觉测算器。我们提出一种方法,利用进化神经网络,从地面实况培训数据中学习对测算器的难以到模型的校正。为此,我们从一个矩阵Li小组方法中得出一个新的损失功能,学习SE(3)校正,并用一种自然配方来平衡翻译和旋转误差。我们用这一损失来训练一个深珀斯校正网络(DPC-Net),以预测某个特定测算器、传感器和环境的校正。我们利用KITTI odorographic数据集,展示了对计算高效的稀薄立式视觉测图管道的准确性的重大改进,从而使它作为现代的计算密集密度测算器。此外,我们展示了如何利用DPC-Net来减轻低校准镜头扭曲参数的影响。