基于拉梅曲线LED的可见光定位：一种通用的相机姿态估计方法
Visible Light Positioning With Lamé Curve LEDs: A Generic Approach for Camera Pose Estimation
AI总结：
本文提出一种基于拉梅曲线LED的通用可见光定位算法LC-VLP，通过统一表示常见LED形状并利用曲线参数进行非线性最小二乘优化，实现高精度相机姿态估计。
摘要：
基于相机的可见光定位（VLP）是一种有前景的技术，可实现精确且低成本的室内相机姿态估计（CPE）。为减少所需发光二极管（LED）的数量，先进方法通常利用LED形状特征进行定位。尽管有趣，但这些方法通常局限于单一LED几何形状，导致在异构LED形状场景中失效。为应对这一挑战，本文研究拉梅曲线作为常见LED形状的统一表示，并提出一种使用拉梅曲线形状LED的通用VLP算法，称为LC-VLP。在所考虑的系统中，多个天花板安装的拉梅曲线形状LED通过可见光通信定期广播其曲线参数，这些参数由配备相机的接收器捕获。基于接收到的LED图像和曲线参数，接收器可使用LC-VLP估计相机姿态。具体而言，离线构建LED数据库以存储曲线参数，而在线定位则被表述为非线性最小二乘问题并迭代求解。为提供可靠的初始化，进一步开发了一种无需对应点的透视n点（FreePnP）算法，无需任何预校准参考点即可实现近似CPE。通过仿真和实验验证了LC-VLP的性能。仿真表明，在圆形和矩形LED场景中，LC-VLP均优于最先进的方法。与透视弧算法相比，LC-VLP可实现平均位置和旋转误差均降低30%以上。实验进一步表明，LC-VLP可实现小于4厘米的平均位置精度。
英文摘要：
Camera-based visible light positioning (VLP) is a promising technique for accurate and low-cost indoor camera pose estimation (CPE). To reduce the number of required light-emitting diodes (LEDs), advanced methods commonly exploit LED shape features for positioning. Although interesting, they are typically restricted to a single LED geometry, leading to failure in heterogeneous LED-shape scenarios. To address this challenge, this paper investigates Lamé curves as a unified representation of common LED shapes and proposes a generic VLP algorithm using Lamé curve-shaped LEDs, termed LC-VLP. In the considered system, multiple ceiling-mounted Lamé curve-shaped LEDs periodically broadcast their curve parameters via visible light communication, which are captured by a camera-equipped receiver. Based on the received LED images and curve parameters, the receiver can estimate the camera pose using LC-VLP. Specifically, an LED database is constructed offline to store the curve parameters, while online positioning is formulated as a nonlinear least-squares problem and solved iteratively. To provide a reliable initialization, a correspondence-free perspective-n-points (FreePnP) algorithm is further developed, enabling approximate CPE without any pre-calibrated reference points. The performance of LC-VLP is verified by both simulations and experiments. Simulations show that LC-VLP outperforms state-of-the-art methods in both circular- and rectangular-LED scenarios. Compared to a perspective arcs algorithm, LC-VLP can achieve reductions of both over 30% in average position and rotation errors. Experiments further show that LC-VLP can achieve an average position accuracy of less than 4 cm.
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