In Monocular Keyframe Visual Simultaneous Localization and Mapping (MKVSLAM) frameworks, when incremental position tracking fails, global pose has to be recovered in a short-time window, also known as short-term relocalization. This capability is crucial for mobile robots to have reliable navigation, build accurate maps, and have precise behaviors around human collaborators. This paper focuses on the development of robust short-term relocalization capabilities for mobile robots using a monocular camera system. A novel multimodal keyframe descriptor is introduced, that contains semantic information of objects detected in the environment and the spatial information of the camera. Using this descriptor, a new Keyframe-based Place Recognition (KPR) method is proposed that is formulated as a multi-stage keyframe filtering algorithm, leading to a new relocalization pipeline for MKVSLAM systems. The proposed approach is evaluated over several indoor GPS denied datasets and demonstrates accurate pose recovery, in comparison to a bag-of-words approach.

翻译：在单目关键帧视觉同时定位与建图（MKVSLAM）框架中，当增量式位置跟踪失败时，必须在短时间窗口内恢复全局位姿，此过程称为短期重定位。该能力对于移动机器人实现可靠导航、构建精确地图以及在人类协作环境中完成精准行为至关重要。本文聚焦于利用单目相机系统为移动机器人开发鲁棒的短期重定位能力。我们提出了一种新颖的多模态关键帧描述符，该描述符包含环境中检测到的物体语义信息与相机空间信息。基于此描述符，本文提出一种新的基于关键帧的地点识别（KPR）方法，该方法被构建为多级关键帧过滤算法，从而形成适用于MKVSLAM系统的新型重定位流程。所提方法在多个室内无GPS数据集上进行评估，与词袋方法相比，展现出精确的位姿恢复性能。