用数学曲线统一建模多种LED形状,实现高精度室内定位
Visible Light Positioning With Lamé Curve LEDs: A Generic Approach for Camera Pose Estimation
- 用Lamé曲线统一表示常见LED形状,支持异构场景
- 实验显示定位误差低于4厘米,比现有方法降低超30%
- 无需预标定点,适合快速部署的智能照明系统
基于可见光的相机定位(VLP)是一种低成本、高精度的室内相机姿态估计(CPE)技术。为减少所需LED数量,先进方法常利用LED形状特征进行定位,但通常仅适用于单一几何形状,在混合形状场景中失效。本文提出将Lamé曲线作为常见LED形状的统一表示,并设计通用的LC-VLP算法。系统中多个吊顶安装的Lamé曲线形LED通过可见光通信周期性广播其曲线参数,由带摄像头的接收器捕获。结合接收到的LED图像与参数,接收器通过构建离线LED数据库,并将在线定位建模为非线性最小二乘问题迭代求解。为实现可靠初始化,进一步提出无对应关系的FreePnP算法,可在无预标定点情况下实现近似姿态估计。仿真与实验验证表明,LC-VLP在圆形与矩形LED场景中均优于现有方法,相较透视弧算法,平均位置与旋转误差均降低超30%;实验结果显示平均定位精度小于4厘米。
原文摘要 · Abstract (English)
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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