Localization and mapping with heterogeneous multi-sensor fusion have been prevalent in recent years. To adequately fuse multi-modal sensor measurements received at different time instants and different frequencies, we estimate the continuous-time trajectory by fixed-lag smoothing within a factor-graph optimization framework. With the continuous-time formulation, we can query poses at any time instants corresponding to the sensor measurements. To bound the computation complexity of the continuous-time fixed-lag smoother, we maintain temporal and keyframe sliding windows with constant size, and probabilistically marginalize out control points of the trajectory and other states, which allows preserving prior information for future sliding-window optimization. Based on continuous-time fixed-lag smoothing, we design tightly-coupled multi-modal SLAM algorithms with a variety of sensor combinations, like the LiDAR-inertial and LiDAR-inertial-camera SLAM systems, in which online timeoffset calibration is also naturally supported. More importantly, benefiting from the marginalization and our derived analytical Jacobians for optimization, the proposed continuous-time SLAM systems can achieve real-time performance regardless of the high complexity of continuous-time formulation. The proposed multi-modal SLAM systems have been widely evaluated on three public datasets and self-collect datasets. The results demonstrate that the proposed continuous-time SLAM systems can achieve high-accuracy pose estimations and outperform existing state-of-the-art methods. To benefit the research community, we will open source our code at ~\url{https://github.com/APRIL-ZJU/clic}.

翻译：异构多传感器融合的定位与建图近年来已广泛应用。为充分融合不同时刻、不同频率获取的多模态传感器测量值，我们基于因子图优化框架，通过固定滞后平滑估计连续时间轨迹。基于连续时间公式，我们可在任意与传感器测量值对应的时间点查询位姿。为限制连续时间固定滞后平滑器的计算复杂度，我们维护恒定规模的时空滑动窗口与关键帧滑动窗口，并通过概率化边缘化轨迹控制点及其他状态，从而为后续滑动窗口优化保留先验信息。基于连续时间固定滞后平滑，我们设计了多种传感器组合的紧耦合多模态SLAM算法，例如LiDAR-惯性及LiDAR-惯性-相机SLAM系统，其中在线时间偏移标定亦得到自然支持。更重要的是，得益于边缘化处理及我们推导的解析雅可比矩阵用于优化，所提连续时间SLAM系统可突破连续时间公式的高复杂度，实现实时性能。所提多模态SLAM系统已在三个公开数据集及自采集数据集上得到广泛评估。结果表明，所提连续时间SLAM系统可实现高精度位姿估计，并超越现有最先进方法。为回馈研究社区，我们将在~\url{https://github.com/APRIL-ZJU/clic} 开源代码。