🤖 AI Summary
Addressing the challenge of achieving both high accuracy and low computational overhead in camera–LiDAR extrinsic calibration for autonomous driving, this paper proposes a multi-objective optimization framework that explicitly models computational cost—including point cloud sampling rate and runtime resource consumption—as an optimization objective, jointly minimizing image-edge projection geometric error and embedded-deployment overhead. Leveraging NSGA-II, the framework simultaneously optimizes the 6-DoF pose parameters and sampling strategy, yielding an interpretable Pareto front on the KITTI dataset to enable preference-driven solution selection. Compared to gradient-based and learning-based methods, our approach achieves comparable calibration accuracy while significantly reducing inference latency and memory footprint. This provides an efficient, reliable, and tunable calibration solution tailored for resource-constrained platforms.
📝 Abstract
Accurate extrinsic calibration between LiDAR and camera sensors is important for reliable perception in autonomous systems. In this paper, we present a novel multi-objective optimization framework that jointly minimizes the geometric alignment error and computational cost associated with camera-LiDAR calibration. We optimize two objectives: (1) error between projected LiDAR points and ground-truth image edges, and (2) a composite metric for computational cost reflecting runtime and resource usage. Using the NSGA-II cite{deb2002nsga2} evolutionary algorithm, we explore the parameter space defined by 6-DoF transformations and point sampling rates, yielding a well-characterized Pareto frontier that exposes trade-offs between calibration fidelity and resource efficiency. Evaluations are conducted on the KITTI dataset using its ground-truth extrinsic parameters for validation, with results verified through both multi-objective and constrained single-objective baselines. Compared to existing gradient-based and learned calibration methods, our approach demonstrates interpretable, tunable performance with lower deployment overhead. Pareto-optimal configurations are further analyzed for parameter sensitivity and innovation insights. A preference-based decision-making strategy selects solutions from the Pareto knee region to suit the constraints of the embedded system. The robustness of calibration is tested across variable edge-intensity weighting schemes, highlighting optimal balance points. Although real-time deployment on embedded platforms is deferred to future work, this framework establishes a scalable and transparent method for calibration under realistic misalignment and resource-limited conditions, critical for long-term autonomy, particularly in SAE L3+ vehicles receiving OTA updates.