We propose a deep learning-based LiDAR odometry estimation method called LoRCoN-LO that utilizes the long-term recurrent convolutional network (LRCN) structure. The LRCN layer is a structure that can process spatial and temporal information at once by using both CNN and LSTM layers. This feature is suitable for predicting continuous robot movements as it uses point clouds that contain spatial information. Therefore, we built a LoRCoN-LO model using the LRCN layer, and predicted the pose of the robot through this model. For performance verification, we conducted experiments exploiting a public dataset (KITTI). The results of the experiment show that LoRCoN-LO displays accurate odometry prediction in the dataset. The code is available at https://github.com/donghwijung/LoRCoN-LO.

翻译：本文提出一种基于深度学习的激光雷达里程计估计方法——LoRCoN-LO，该方法利用长期递归卷积网络（LRCN）结构。LRCN层是一种能够通过结合CNN（卷积神经网络）与LSTM（长短期记忆网络）层同时处理时空信息的结构。该特性适用于利用包含空间信息的点云数据预测连续机器人运动。为此，我们构建了基于LRCN层的LoRCoN-LO模型，并通过该模型实现机器人位姿预测。为验证性能，我们利用公开数据集（KITTI）进行实验。实验结果表明，LoRCoN-LO在该数据集上展现出准确的里程计预测能力。代码开源于https://github.com/donghwijung/LoRCoN-LO。