Uploaded October 2025 | Updated September 2026, 3 weeks ago
Fixed-lag Radar-LiDAR-Inertial smoothers conventionally create one factor graph node per measurement to compensate for the lack of time synchronization between radar and LiDAR. For a radar-LiDAR sensor pair with equal rates, this strategy results in a state creation rate of twice the individual sensor frequencies. This doubling of the number of states per second yields high optimization costs, inhibiting real-time performance on resource-constrained hardware. We introduce IMU-preintegrated radar factors that use high-rate inertial data to propagate the most recent LiDAR state to the radar measurement timestamp. This strategy maintains the node creation rate at the LiDAR measurement frequency. Assuming equal sensor rates, this lowers the number of nodes by 50% and consequently the computational costs. Experiments on a single board computer (which has 4 cores each of 2.2 GHz A73 and 2GHz A53 with 8 GB RAM) show that our method preserves the absolute pose error of a conventional baseline while simultaneously lowering the aggregated factor graph optimization time by up to 56%.
Fixed-lag Radar-LiDAR-Inertial smoothers conventionally create one factor graph node per measurement to compensate for the lack of time synchronization between radar and LiDAR. For a radar-LiDAR sensor pair with equal rates, this strategy results in a state creation rate of twice the individual sensor frequencies. This doubling of the number of states per second yields high optimization costs, inhibiting real-time performance on resource-constrained hardware. We introduce IMU-preintegrated radar factors that use high-rate inertial data to propagate the most recent LiDAR state to the radar measurement timestamp. This strategy maintains the node creation rate at the LiDAR measurement frequency. Assuming equal sensor rates, this lowers the number of nodes by 50% and consequently the computational costs. Experiments on a single board computer (which has 4 cores each of 2.2 GHz A73 and 2GHz A53 with 8 GB RAM) show that our method preserves the absolute pose error of a conventional baseline while simultaneously lowering the aggregated factor graph optimization time by up to 56%.
