Deep visual odometry, despite extensive research, still faces limitations in accuracy and generalizability that prevent its broader application. To address these challenges, we propose an Oriented FAST and Rotated BRIEF (ORB)-guided visual odometry with selective online adaptation named ORB-SfMLearner. We present a novel use of ORB features for learning-based ego-motion estimation, leading to more robust and accurate results. We also introduce the cross-attention mechanism to enhance the explainability of PoseNet and have revealed that driving direction of the vehicle can be explained through the attention weights. To improve generalizability, our selective online adaptation allows the network to rapidly and selectively adjust to the optimal parameters across different domains. Experimental results on KITTI and vKITTI datasets show that our method outperforms previous state-of-the-art deep visual odometry methods in terms of ego-motion accuracy and generalizability. Code is available at https://github.com/PeaceNeil/ORB-SfMLearner

翻译：尽管已有广泛研究，深度视觉里程计在精度与泛化能力方面仍存在局限，阻碍了其更广泛的应用。为应对这些挑战，我们提出了一种基于定向FAST与旋转BRIEF（ORB）引导、具备选择性在线自适应能力的视觉里程计方法，命名为ORB-SfMLearner。我们提出了一种新颖的ORB特征使用方法，用于基于学习的自运动估计，从而获得更鲁棒且更精确的结果。我们还引入了交叉注意力机制以增强PoseNet的可解释性，并揭示了车辆的行驶方向可通过注意力权重进行解释。为提升泛化能力，我们的选择性在线自适应机制使网络能够快速、选择性地调整至跨不同域的最优参数。在KITTI和vKITTI数据集上的实验结果表明，本方法在自运动估计精度与泛化能力方面均优于以往最先进的深度视觉里程计方法。代码公开于 https://github.com/PeaceNeil/ORB-SfMLearner