cross-device alignment

This work addresses the challenge of time synchronization among heterogeneous sensors in roadside and vehicle-mounted multi-LiDAR–multi-camera systems by proposing an open-source, modular, and scalable hardware synchronization solution. Using the LiDAR synchronization pulse as a reference, the system employs programmable delay circuits to generate independent trigger signals for each camera, enabling flexible and precise spatiotemporal alignment. The architecture supports arbitrary combinations of sensor counts and has been validated on both a three-camera roadside platform and a seven-camera vehicular setup. Experimental results demonstrate significantly improved spatial consistency between point clouds and images, while the design ensures robustness, reproducibility, and ease of deployment.

High-precision online estimation algorithms for robotics are highly sensitive to sensor timestamp accuracy; however, existing synchronization solutions struggle to simultaneously achieve real-time operation, low cost, and high temporal precision. To address this, we propose a real-time, trigger-based time synchronization system built on commodity hardware. Our approach employs a hardware-triggered mechanism to jointly schedule heterogeneous sensors operating at different frequencies, and integrates an enhanced clock synchronization protocol with nanosecond-resolution timestamping to ensure precise coordination between sensors and the onboard computer. Crucially, the system eliminates reliance on expensive dedicated timing hardware, thereby substantially mitigating the impact of timing errors on online estimation. Experimental evaluation on a physical robot platform demonstrates sub-microsecond synchronization accuracy, along with significant improvements in both estimation robustness and real-time performance.

Heterogeneous camera systems—e.g., visible-light/infrared, professional/consumer-grade, or audio-equipped/audio-less setups—lack hardware synchronization in real-world scenarios, leading to significant spatiotemporal misalignment across multi-view videos. Method: This paper proposes a vision-based time-encoding method leveraging a custom-designed LED Clock. By embedding temporal exposure timestamps within frames using red and infrared LEDs, the approach achieves cross-modal, audio-free, and external-timecode-free millisecond-level synchronization. It further integrates RMSE-optimized temporal alignment with joint multi-device calibration. Contribution/Results: The method reduces synchronization residuals to 1.34 ms—substantially outperforming existing optical signal, audio-based, and timecode synchronization schemes. Validated in large-scale surgical recordings involving over 25 heterogeneous cameras, it significantly improves downstream tasks including multi-view 3D reconstruction and pose estimation.

Joint calibration of temporal delay and rotational extrinsics between event cameras and multi-sensor systems remains challenging due to the absence of dedicated calibration targets. Method: We propose a motion-driven, target-free calibration framework that uniquely integrates canonical correlation analysis (CCA) with continuous-time SO(3) nonlinear optimization. Angular velocity is estimated from optical flow derived from event streams; CCA initializes time delay and rotation parameters, which are then jointly refined via continuous-time SO(3) pose modeling. Contribution/Results: The method eliminates reliance on calibration boards while achieving accuracy comparable to board-based approaches—angular error < 0.5° and time-delay error < 1 ms—on both public and in-house datasets. It significantly outperforms pure-CCA baselines, offering high robustness, precision, and practical deployment flexibility.

Existing asynchronous event-camera–IMU tightly coupled odometry methods suffer from limited accuracy and latency under high-speed motion and high-dynamic-range (HDR) conditions, primarily due to reliance on conventional discrete-time preintegration models. This paper proposes the Gaussian Process Pre-optimization (GPO) framework—a continuous-time formulation enabling analytically tractable state and Jacobian propagation at arbitrary timestamps. GPO introduces temporal Gaussian processes (TGPs) for continuous preintegration, achieving linear optimization complexity and constant-time query capability. Leveraging a lightweight two-stage optimization and asynchronous event–inertial tight coupling within a filtering paradigm, GPO natively supports fully asynchronous sensor fusion. Evaluated on both public and in-house datasets, GPO consistently improves localization accuracy and computational efficiency over state-of-the-art asynchronous fusion approaches, demonstrating superior overall performance.

This work addresses the degradation in fusion quality arising from spatiotemporal heterogeneity in vehicular collaborative perception, caused by clock asynchrony, communication delays, and motion discrepancies. To mitigate these issues, the authors propose a dynamic compensation method that jointly models network time synchronization and Age of Information (AoI). By establishing a unified time reference and leveraging AoI to estimate communication latency, the approach enables precise spatiotemporal alignment of multi-vehicle perception features. Furthermore, it performs uncertainty-aware dynamic weighted fusion based on alignment quality and AoI. This is the first method to synergistically integrate network synchronization with AoI modeling for compensating time-varying clock drift and communication delays. Experimental results in simulated environments with clock drift and link delays demonstrate significant improvements over existing baselines, effectively enhancing the consistency and accuracy of collaborative perception.

This work proposes a fully fixed-point, non-iterative, streaming optical flow algorithm tailored for efficient deployment on resource-constrained FPGAs. By partitioning asynchronous event streams into fixed-time windows and representing them as 1-bit spatial occupancy grids, the method evaluates multiple velocity hypotheses in parallel using only integer logic—comprising shift registers, counters, comparators, and LUT-based multipliers—without requiring frame reconstruction, floating-point arithmetic, or division operations. A single-axis prototype was successfully implemented on a Xilinx Artix-7 FPGA, occupying less than 2 kB of memory and achieving 99.5% directional accuracy under event densities of 10–40%. To the best of our knowledge, this is the first demonstration of low-latency, sparse velocity estimation on an FPGA without relying on DSP blocks or dedicated dividers.

Indoor visual localization is hindered by detection noise, occlusions, and limited camera coverage, leading to multi-stage uncertainties that existing fusion methods fail to explicitly model. This work proposes a component-level error quantification and calibration mechanism that explicitly characterizes the uncertainty in homography calibration, human detection, and motion tracking, and leverages these estimates to optimize multi-camera fusion weights. By transforming the fusion process from a black-box into an interpretable framework, the method significantly enhances trajectory stability and motion smoothness. Experimental results demonstrate that, while yielding only marginal gains in absolute localization accuracy over single-camera baselines, the proposed strategy effectively reduces trajectory variance and substantially improves the continuity and robustness of motion estimation.