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Localization and mapping with heterogeneous multi-sensor fusion have beenprevalent in recent years. To adequately fuse multi-modal sensor measurementsreceived at different time instants and different frequencies, we estimate thecontinuous-time trajectory by fixed-lag smoothing within a factor-graphoptimization framework. With the continuous-time formulation, we can queryposes at any time instants corresponding to the sensor measurements. To boundthe computation complexity of the continuous-time fixed-lag smoother, wemaintain temporal and keyframe sliding windows with constant size, andprobabilistically marginalize out control points of the trajectory and otherstates, which allows preserving prior information for future sliding-windowoptimization. Based on continuous-time fixed-lag smoothing, we designtightly-coupled multi-modal SLAM algorithms with a variety of sensorcombinations, like the LiDAR-inertial and LiDAR-inertial-camera SLAM systems,in which online timeoffset calibration is also naturally supported. Moreimportantly, benefiting from the marginalization and our derived analyticalJacobians for optimization, the proposed continuous-time SLAM systems canachieve real-time performance regardless of the high complexity ofcontinuous-time formulation. The proposed multi-modal SLAM systems have beenwidely evaluated on three public datasets and self-collect datasets. Theresults demonstrate that the proposed continuous-time SLAM systems can achievehigh-accuracy pose estimations and outperform existing state-of-the-artmethods. To benefit the research community, we will open source our code at~\url{https://github.com/APRIL-ZJU/clic}.

Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM