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

TL;DR

This paper introduces a continuous-time fixed-lag smoothing approach for multi-sensor SLAM that enables real-time, high-accuracy localization by efficiently fusing heterogeneous sensor data at different times.

Contribution

It proposes a novel continuous-time fixed-lag smoothing framework within a factor-graph for multi-modal SLAM, supporting online time-offset calibration and real-time performance.

Findings

Achieves high-accuracy pose estimation on multiple datasets.

Outperforms existing state-of-the-art SLAM methods.

Supports real-time operation with complex continuous-time models.

Abstract

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}.