Feature extraction and matching are the basic parts of many computer vision tasks, such as 2D or 3D object detection, recognition, and registration. As we all know, 2D feature extraction and matching have already been achieved great success. Unfortunately, in the field of 3D, the current methods fail to support the extensive application of 3D LiDAR sensors in vision tasks, due to the poor descriptiveness and inefficiency. To address this limitation, we propose a novel 3D feature representation method: Linear Keypoints representation for 3D LiDAR point cloud, called LinK3D. The novelty of LinK3D lies in that it fully considers the characteristics (such as sparsity, complexity of scenarios) of LiDAR point cloud, and represents current keypoint with its robust neighbor keypoints, which provide strong constraint on the description of current keypoint. The proposed LinK3D has been evaluated on two public datasets (i.e., KITTI, Steven VLP16), and the experimental results show that our method greatly outperforms the state-of-the-arts in matching performance. More importantly, LinK3D shows excellent real-time performance (based on the frequence 10 Hz of LiDAR). LinK3D only takes an average of 32 milliseconds to extract features from the point cloud collected by a 64-ray laser beam, and takes merely about 8 milliseconds to match two LiDAR scans when executed in a notebook with an Intel Core i7 @2.2 GHz processor. Moreover, our method can be widely extended to a variety of 3D vision applications. In this paper, we has applied our LinK3D to 3D registration, LiDAR odometry and place recognition tasks, and achieved competitive results compared with the state-of-the-art methods.
翻译:功能提取和匹配是许多计算机视觉任务的基本部分, 如 2D 或 3D 对象检测、 识别和注册。 众所周知, 2D 特征提取和匹配已经取得了巨大成功 。 不幸的是, 在 3D 领域, 目前的方法无法支持在视觉任务中广泛应用 3D LiDAR 传感器。 为解决这一限制, 我们提议了一个新型的 3D 特征代表法: 3D LiD2.2 点云的线性基点代表, 称为 LinK3D 。 林K3D 的新颖在于它充分考虑利D 点云的特性( 如 屏幕性、 情景的复杂性), 并且代表着当前关键点, 因为它的周围关键点对当前关键点的描述有强烈的制约 。 拟议的 LinK3D 在两个公开的激光数据集( 如 KITTI、 Steve VLPP16) 上, 实验结果显示, 我们的方法在匹配性运行的状态中大大优于状态应用 3D 。 。 林K 3D 显示, 平均的性运行到 。 在 3D 上, 只有 的 的 3D 以 以 3D 以 3D 水平 以 以 的 的 3D 3D 的 以 以 的 的 度 的 的 度 的 以 的 的 的 3D 的 的 的 向为 。