NN-PL-VIO: A Customizable Neural Network Based VIO Framework with a Lightweight Point-Line Joint Network

Harnessing the potential of line features to enhance the localization accuracy of point-based Visual-Inertial SLAM (VINS) has become a focus due to the additional constraints they provide on scene structure, especially in environments with low texture. However, the challenge of real-time performance when integrating line features into VINS remains unaddressed. This paper introduces NN-PL-VIO, a customizable real-time Visual-Inertial Odometry (VIO) system designed for embedded devices to achieve both accuracy and efficiency in point-line feature extraction. This framework facilitates the performance testing of various point, line extraction, and matching methods in a positioning system for multi-feature joint localization. In addition, to offer real-time methods, we propose SuperPLNet, a self-supervised fusion network for joint point-line detection and description. Experiments on the Euroc Dataset show that the accuracy surpasses the baseline by 20% in difficult scenes with a processing speed of up to 11.7fps on embedded systems. The source code of our method is available at: https://github.com/efc-robot/NN-PL-VIO