A Computationally Aware Multi Objective Framework for Camera LiDAR Calibration

TL;DR

This paper introduces a multi-objective optimization framework for camera-LiDAR calibration that balances accuracy and computational efficiency, enabling adaptable and resource-aware calibration solutions for autonomous systems.

Contribution

It presents a novel multi-objective optimization approach using NSGA-II to explore calibration trade-offs, providing a scalable, interpretable, and resource-efficient calibration method.

Findings

Demonstrates lower deployment overhead compared to existing methods.

Provides a Pareto frontier illustrating trade-offs between accuracy and efficiency.

Validates approach on KITTI dataset with robust calibration results.

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.