Vision-aided localization for low-cost mobile robots in diverse environments has attracted widespread attention recently. Although many current systems are applicable in daytime environments, nocturnal visual localization is still an open problem owing to the lack of stable visual information. An insight from most nocturnal scenes is that the static and bright streetlights are reliable visual information for localization. Hence we propose a nocturnal vision-aided localization system in streetlight maps with a novel data association and matching scheme using object detection methods. We leverage the Invariant Extended Kalman Filter (InEKF) to fuse IMU, odometer, and camera measurements for consistent state estimation at night. Furthermore, a tracking recovery module is also designed for tracking failures. Experiments on multiple real nighttime scenes validate that the system can achieve remarkably accurate and robust localization in nocturnal environments.

翻译：摘要：面向低成本移动机器人在多样化环境中的视觉辅助定位近期受到广泛关注。尽管现有系统多适用于日间环境，但夜间视觉定位因缺乏稳定视觉信息仍属开放性问题。多数夜间场景的洞察表明：静态明亮的街灯可作为定位的可靠视觉信息。为此，我们提出一种基于街灯地图的夜间视觉辅助定位系统，该方法采用新颖的数据关联与匹配方案，通过目标检测技术实现。我们利用不变扩展卡尔曼滤波融合IMU、里程计及相机测量值，实现夜间状态估计的一致性。此外，针对跟踪失败问题设计了跟踪恢复模块。在多个真实夜间场景中的实验验证了该系统在夜间环境下可达到显著精确且鲁棒的定位性能。