Indoor localization from a single RGB image via Perspective-n-Lines (PnL) remains a fundamental yet challenging problem in computer vision. To address this, the PnL-IOC algorithm was introduced, demonstrating remarkable performance by jointly estimating 3D correspondences of image outer corners (IOCs)—the intersection points between image borders and room layout boundaries—while optimizing camera pose within an iterative Gauss-Newton framework. However, existing learning-based indoor layout estimation methods often struggle to achieve high accuracy, resulting in unreliable line correspondences, which significantly limits the effectiveness of PnL-IOC. To overcome this limitation, we propose InPro-PnL, a probabilistic PnL layer for indoor camera pose estimation. Our approach introduces a novel framework that models the pose distribution on the SE(3) manifold, effectively extending the categorical Softmax function into the continuous domain. This is achieved by integrating a probabilistic PnL layer, where denoised 2D-3D correspondences serve as intermediate variables and are optimized by minimizing the KL divergence between the predicted and target pose distributions. Experimental results demonstrate that our method outperforms the geometric PnL-IOC approach and holds significant potential for further enhancing room layout estimation accuracy.