Precise localization using visual sensors is a fundamental requirement in many applications, including robotics, augmented reality, and autonomous systems. Traditionally, the localization problem has been tackled by leveraging 3D-geometry registering approaches. Recently, end-to-end regressor strategies using deep convolutional neural networks have achieved impressive performance, but they do not achieve the same performance as 3D structure-based methods. To some extent, this problem has been tackled by leveraging the beneficial properties of sequential images or geometric constraints. However, these approaches can only achieve a slight improvement. In this work, we address this problem for indoor scenarios, and we argue that regressing the camera pose using sparse feature descriptors could significantly improve the pose regressor performance compared with deep single-feature-vector representation. We propose a novel approach that can directly consume sparse feature descriptors to regress the camera pose effectively. More importantly, we propose a simplistic data augmentation procedure to exploit the sparse descriptors of unseen poses, leading to a remarkable enhancement in the generalization performance. Lastly, we present an extensive evaluation of our method on publicly available indoor datasets. Our FeatLoc achieves 22% and 40% improvements in translation errors on 7-Scenes and 12-Scenes relatively, compared with recent state-of-the-art absolute pose regression-based approaches. Our codes are released at https://github.com/ais-lab/FeatLoc.

使用视觉传感器进行精确定位是许多应用的基本要求，包括机器人、增强现实和自主系统。传统上，定位问题是通过利用 3D 几何配准方法来解决的。最近，使用深度卷积神经网络的端到端回归器策略取得了令人印象深刻的性能，但它们没有达到与基于 3D 结构的方法相同的性能。在某种程度上，这个问题已经通过利用顺序图像或几何约束的有益特性得到了解决。但是，这些方法只能取得轻微的改进。在这项工作中，我们针对室内场景解决了这个问题，我们认为，与深度单特征向量表示相比，使用稀疏特征描述符回归相机位姿可以显着提高位姿回归器的性能。我们提出了一种新颖的方法，可以直接使用稀疏特征描述符来有效地回归相机姿态。更重要的是，我们提出了一种简单的数据增强程序来利用看不见的姿势的稀疏描述符，从而显着提高泛化性能。最后，我们在公开的室内数据集上对我们的方法进行了广泛的评估。与最近最先进的基于绝对姿态回归的方法相比，我们的 FeatLoc 在 7-Scenes 和 12-Scenes 的翻译错误上实现了 22% 和 40% 的相对改进。我们的代码在 https://github.com/ais-lab/FeatLoc 发布。