This paper presents PAL-SLAM2, a visual and visual–inertial monocular simultaneous localization and mapping (SLAM) system for a panoramic annular lens (PAL) with an ultra-hemispherical field of view (FoV), overcoming the limitations of traditional frameworks in handling fast turns, nighttime conditions and rapid lighting changes. The system incorporates modules for initialization, tracking, local mapping, loop and map merging. To fully exploit information from the negative space (), keypoints are projected onto the unit sphere for visual initialization and tightly-coupled visual–inertial optimization. Leveraging a multimap strategy, the feature-based PAL-SLAM2 is capable of detecting unidirectional and bidirectional public areas using PAL images, thereby enhancing the performance of loop correction and map merging. Testing indicates that it achieves an average accuracy of 7.1 cm on the PALVIO indoor dataset, surpassing similar state-of-the-art frameworks. Furthermore, we have collected a large-scale dataset comprising over 120,000 images with corresponding inertial measurement unit (IMU) data, demonstrating PAL-SLAM2’s robustness under challenging outdoor conditions. Relying solely on visual and inertial inputs, our system is particularly suited for environments where Global Navigation Satellite System (GNSS) signals are frequently obstructed, such as indoor spaces or dense urban areas. The dataset can be accessed at: .

中文翻译：

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PAL-SLAM2：全景环形镜头的视觉和视觉-惯性单目SLAM

本文提出了PAL-SLAM2，一种用于具有超半球视场（FoV）的全景环形镜头（PAL）的视觉和视觉惯性单目同步定位与建图（SLAM）系统，克服了传统框架在处理方面的局限性。快速转弯、夜间条件和快速的照明变化。该系统包含用于初始化、跟踪、本地映射、循环和地图合并的模块。为了充分利用负空间 () 中的信息，关键点被投影到单位球体上以进行视觉初始化和紧密耦合的视觉惯性优化。基于特征的PAL-SLAM2利用多地图策略，能够利用PAL图像检测单向和双向公共区域，从而增强环路校正和地图合并的性能。测试表明，它在 PALVIO 室内数据集上的平均精度达到 7.1 厘米，超越了同类最先进的框架。此外，我们还收集了一个包含超过 120,000 张图像以及相应惯性测量单元 (IMU) 数据的大规模数据集，证明了 PAL-SLAM2 在具有挑战性的室外条件下的鲁棒性。我们的系统仅依靠视觉和惯性输入，特别适合全球导航卫星系统 (GNSS) 信号经常受阻的环境，例如室内空间或密集的城市地区。该数据集可通过以下网址访问： 。