LoRCoN-LO: Long-term Recurrent Convolutional Network-based LiDAR Odometry
This work addresses robot localization for autonomous navigation, but it appears incremental as it applies an existing LRCN structure to LiDAR odometry without claiming major breakthroughs.
The paper tackles LiDAR odometry estimation by proposing LoRCoN-LO, a deep learning method using a long-term recurrent convolutional network (LRCN) to process spatial and temporal information from point clouds for predicting robot pose. Experimental results on the KITTI dataset show accurate odometry prediction, though no concrete numbers are provided.
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.