论文标题
Lodonet：具有2D键点匹配的深神经网络，用于3D激光射击估计
LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation
论文作者
论文摘要
基于深度学习的激光探测器（LO）估计吸引了自主驾驶和机器人技术领域的研究兴趣。现有作品将连续的激光镜头框架送入神经网络，作为点云和匹配对的点云。相比之下，是由基于图像的特征提取器成功的动机，我们建议将激光镜框架转移到图像空间，并将问题重新制定为图像特征提取。在用于特征提取的规模不变特征变换（SIFT）的帮助下，我们能够生成匹配的关键点对（MKP），可以精确地返回到3D空间。卷积神经网络管道设计用于通过提取的MKPS估算LiDAR的射频估计。然后，在Kitti Odometry估计基准中评估了所提出的方案，即Lodonet，与最新的成绩相比，以相当的效果甚至更好。
Deep learning based LiDAR odometry (LO) estimation attracts increasing research interests in the field of autonomous driving and robotics. Existing works feed consecutive LiDAR frames into neural networks as point clouds and match pairs in the learned feature space. In contrast, motivated by the success of image based feature extractors, we propose to transfer the LiDAR frames to image space and reformulate the problem as image feature extraction. With the help of scale-invariant feature transform (SIFT) for feature extraction, we are able to generate matched keypoint pairs (MKPs) that can be precisely returned to the 3D space. A convolutional neural network pipeline is designed for LiDAR odometry estimation by extracted MKPs. The proposed scheme, namely LodoNet, is then evaluated in the KITTI odometry estimation benchmark, achieving on par with or even better results than the state-of-the-art.
