Precise and real-time localization of unmanned aerial vehicles (UAVs) or robots in GNSS denied indoor environments are critically important for various logistics and surveillance applications. Vision-based simultaneously locating and mapping (VSLAM) are key solutions but suffer location drifts in texture-less, man-made indoor environments. Line features are rich in man-made environments which can be exploited to improve the localization robustness, but existing point-line based VSLAM methods still lack accuracy and efficiency for the representation of lines introducing unnecessary degrees of freedoms. In this paper, we propose Inverse Depth Line Localization(IDLL), which models each extracted line feature using two inverse depth variables exploiting the fact that the projected pixel coordinates on the image plane are rather accurate, which partially restrict the lines. This freedom-reduced representation of lines enables easier line determination and faster convergence of bundle adjustment in each step, therefore achieves more accurate and more efficient frame-to-frame registration and frame-to-map registration using both point and line visual features. We redesign the whole front-end and back-end modules of VSLAM using this line model. IDLL is extensively evaluated in multiple perceptually-challenging datasets. The results show it is more accurate, robust, and needs lower computational overhead than the current state-of-the-art of feature-based VSLAM methods.

翻译：无人机或机器人在GNSS拒止的室内环境中实现精确、实时的定位，对各类物流与监控应用至关重要。基于视觉的同时定位与建图（VSLAM）是关键解决方案，但在无纹理的人造室内环境中易出现位置漂移问题。人造环境中的线特征丰富，可用于提升定位鲁棒性，然而现有基于点-线的VSLAM方法在线条表示时引入了不必要的自由度，导致精度与效率不足。本文提出逆深度线定位（IDLL）方法，该方法利用图像平面上投影像素坐标较为精确这一事实，通过两个逆深度变量对提取的每条线特征进行建模，从而部分约束线条。这种自由度缩减的线条表示简化了线的确定过程，并加速了各步骤中的光束法平差收敛，因此能够基于点与线两类视觉特征实现更精确、更高效的帧间配准与帧-地图配准。我们基于该线模型重新设计了VSLAM的前端与后端模块。在多个感知挑战性数据集上对IDLL进行了全面评估，结果表明，与当前最先进的基于特征的VSLAM方法相比，IDLL具有更高的精度、更强的鲁棒性以及更低的计算开销。