Abstract
Accurate and robust state estimation at nighttime is essential for autonomous robotic navigation to achieve nocturnal or round-the-clock tasks. An intuitive question arises: Can low-cost standard cameras be exploited for nocturnal state estimation? Regrettably, most existing visual methods may fail under adverse illumination conditions, even with active lighting or image enhancement. A pivotal insight, however, is that streetlights in most urban scenarios act as stable and salient prior visual cues at night, reminiscent of stars in deep space aiding spacecraft voyage in interstellar navigation. Inspired by this, we propose Night-Voyager, an object-level nocturnal vision-aided state estimation framework that leverages prior object maps and keypoints for versatile localization. We also find that the primary limitation of conventional visual methods under poor lighting conditions stems from the reliance on pixel-level metrics. In contrast, metric-agnostic, non-pixel-level object detection serves as a bridge between pixel-level and object-level spaces, enabling effective propagation and utilization of object map information within the system. Night-Voyager begins with a fast initialization to solve the global localization problem. By employing an effective two-stage cross-modal data association, the system delivers globally consistent state updates using map-based observations. To address the challenge of significant uncertainties in visual observations at night, a novel matrix Lie group formulation and a feature-decoupled multi-state invariant filter are introduced, ensuring consistent and efficient estimation. Through comprehensive experiments in both simulation and diverse real-world scenarios (spanning approximately 12.3 km), Night-Voyager showcases its efficacy, robustness, and efficiency, filling a critical gap in nocturnal vision-aided state estimation.
Abstract (translated)
夜间精确且稳健的状态估计对于自主机器人导航完成夜行或全天候任务至关重要。一个直观的问题随之而来：低成本的标准摄像头能否用于夜间状态估算？遗憾的是，大多数现有的视觉方法在不利的照明条件下可能会失效，即使使用主动照明或图像增强也不例外。然而，一项关键见解是，在多数城市场景中，街灯作为稳定的、显著的先验视觉线索，在夜晚的作用类似于深空中的星星帮助航天器进行星际导航。受此启发，我们提出了Night-Voyager框架——一种基于对象级别的夜间辅助视觉状态估计框架，利用了先验物体地图和关键点以实现多样化的定位功能。我们还发现，传统的视觉方法在照明条件较差时的主要限制在于依赖像素级的度量标准。相比之下，非像素级、无度量感知的对象检测充当了像素级与对象级空间之间的桥梁，使得系统能够有效传播并利用物体地图信息。 Night-Voyager框架以快速初始化开始，用于解决全局定位问题。通过采用有效的双阶段跨模态数据关联技术，该系统能够在基于地图的观测下提供一致的状态更新。为了解决夜间视觉观察中存在显著不确定性的挑战，引入了一种新颖的矩阵李群形式化和特征解耦的多状态不变滤波器，确保了估计的一致性和效率。 通过在模拟和多样化的真实场景（总计约12.3公里）中的全面实验，Night-Voyager展示了其在夜间视觉辅助状态估算方面的有效性、稳健性及高效性，填补了该领域的一个重要空白。
URL
https://arxiv.org/abs/2502.20054
