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:近些年来,使用多种传感器混凝土进行本地化和绘图的做法十分普遍。为了充分结合在不同时间、瞬时和不同频率收到的多式传感器测量,我们通过在因子色优化框架内使用固定炉渣平滑法来估计连续时间轨迹。随着连续时间的配制,我们可以随时根据感测测量进行配置。为了将连续时间固定炉渣平滑器的计算复杂性捆绑起来,我们保持固定和键盘滑动窗口的不变大小,并有可能将轨迹和其他状态的控制点排斥在外,从而能够保存先前的信息,用于今后的滑动窗面优化。基于连续时间固定炉平滑,我们设计了连续的固定炉渣平滑的连续时间轨迹轨迹。我们设计了各种传感器组合,如LIDAR-内脏和LDAR-内脏隔膜系统,在线定时校校校校校校校也自然得到支持。更重要的是,由于轨的边缘化和我们的分析源雅各布,拟议的连续的SLMM系统可以在实时社区中完成实时的SLA-M-ROM-S-RO-RO-RO-S-RO-S-S-S-ROD-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SD-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SD-SD-S-S-S-S-S-S-SD-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SD-S-SDM-SD-SD-I-SD-S-S-S-SD-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-