论文标题
2D激光雷达和相机融合的室内布局估算
Indoor Layout Estimation by 2D LiDAR and Camera Fusion
论文作者
论文摘要
本文通过融合了一系列捕获的图像和LIDAR数据集，提出了一种用于室内布局估计和重建的算法。在拟议的系统中，一个可移动平台同时收集强度图像和2D激光雷达信息。姿势估计和语义分割是通过将激光雷达点与图像中的线段对齐来共同计算的。对于墙壁正交到地板的室内场景，对齐问题被解耦到自上而下的视图投影和2D相似性转换估计，并通过递归随机样品共识（R-Ransac）算法解决。可以通过在平台在整个环境中移动新扫描来集成新扫描，从而生成，评估和优化假设。所提出的方法避免了需要进行广泛的先前训练或立方布局假设，与大多数以前的室内布局估计方法相比，这更有效和实用。多传感器融合允许提供准确的深度估计和高分辨率的视觉信息。
This paper presents an algorithm for indoor layout estimation and reconstruction through the fusion of a sequence of captured images and LiDAR data sets. In the proposed system, a movable platform collects both intensity images and 2D LiDAR information. Pose estimation and semantic segmentation is computed jointly by aligning the LiDAR points to line segments from the images. For indoor scenes with walls orthogonal to floor, the alignment problem is decoupled into top-down view projection and a 2D similarity transformation estimation and solved by the recursive random sample consensus (R-RANSAC) algorithm. Hypotheses can be generated, evaluated and optimized by integrating new scans as the platform moves throughout the environment. The proposed method avoids the need of extensive prior training or a cuboid layout assumption, which is more effective and practical compared to most previous indoor layout estimation methods. Multi-sensor fusion allows the capability of providing accurate depth estimation and high resolution visual information.
