OKVIS2-X：可配置稠密深度或激光雷达及GNSS的开源基于关键帧的视觉惯性SLAM
OKVIS2-X: Open Keyframe-based Visual-Inertial SLAM Configurable with Dense Depth or LiDAR, and GNSS
- Mobile Robotics Lab, School of Computation, Information and Technology (CIT) at the Technical University of Munich (TUM)（慕尼黑技术大学移动机器人实验室，计算信息与技术学院）
- Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
- Mobile Robotics Lab, ETH Zurich（苏黎世联邦理工学院移动机器人实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出多传感器SLAM系统OKVIS2-X，集成视觉、惯性、深度、激光雷达、GNSS等多模态传感器，采用稠密体素地图与子地图策略，通过紧耦合与在线标定提升精度，在多个基准测试中取得领先性能，可生成全局一致地图用于自主导航。
AI中文摘要：
为赋予移动机器人可用地图以及最高的状态估计精度和鲁棒性，我们提出OKVIS2-X：这是一套顶尖的多传感器同时定位与建图（SLAM）系统，可构建稠密体素占据地图，同时能扩展至大环境并实时运行。我们的统一SLAM框架无缝集成了多种传感器模态：视觉、惯性、测量或学习得到的深度、激光雷达以及全球导航卫星系统（GNSS）测量数据。与大多数顶尖SLAM系统不同，我们主张在利用深度或距离感知能力时使用稠密体素地图表示。我们采用了高效的子地图构建策略，使系统能够扩展至大环境，在最长9公里的序列中得到了验证。OKVIS2-X通过地图对齐因子将估计器与子地图紧耦合，从而提升了精度与鲁棒性。我们的系统可生成全局一致的地图，可直接用于自主导航。为进一步提升OKVIS2-X的精度，我们还加入了相机外参在线标定的可选功能。在EuRoC数据集上，本系统相较于现有顶尖方案实现了最高的轨迹精度；在Hilti22纯视觉惯性基准测试中优于所有竞品，同时在激光雷达版本中也具备竞争力；在VBR数据集的多样大规模序列中展现出顶尖精度。
英文摘要：
To empower mobile robots with usable maps as well as highest state estimation accuracy and robustness, we present OKVIS2-X: a state-of-the-art multi-sensor Simultaneous Localization and Mapping (SLAM) system building dense volumetric occupancy maps, while scalable to large environments and operating in realtime. Our unified SLAM framework seamlessly integrates different sensor modalities: visual, inertial, measured or learned depth, LiDAR and Global Navigation Satellite System (GNSS) measurements. Unlike most state-of-the-art SLAM systems, we advocate using dense volumetric map representations when leveraging depth or range-sensing capabilities. We employ an efficient submapping strategy that allows our system to scale to large environments, showcased in sequences of up to 9 kilometers. OKVIS2-X enhances its accuracy and robustness by tightly-coupling the estimator and submaps through map alignment factors. Our system provides globally consistent maps, directly usable for autonomous navigation. To further improve the accuracy of OKVIS2-X, we also incorporate the option of performing online calibration of camera extrinsics. Our system achieves the highest trajectory accuracy in EuRoC against state-of-the-art alternatives, outperforms all competitors in the Hilti22 VI-only benchmark, while also proving competitive in the LiDAR version, and showcases state of the art accuracy in the diverse and large-scale sequences from the VBR dataset.
