Abstract
Mobile platform visual image sequence inevitably has large areas with various types of weak textures, which affect the acquisition of accurate pose in the subsequent platform moving process. The visual–inertial odometry (VIO) with point features and line features as visual information shows a good performance in weak texture environments, which can solve these problems to a certain extent. However, the extraction and matching of line features are time consuming, and reasonable weights between the point and line features are hard to estimate, which makes it difficult to accurately track the pose of the platform in real time. In order to overcome the deficiency, an improved effective point–line visual–inertial odometry system is proposed in this paper, which makes use of geometric information of line features and combines with pixel correlation coefficient to match the line features. Furthermore, this system uses the Helmert variance component estimation method to adjust weights between point features and line features. Comprehensive experimental results on the two datasets of EuRoc MAV and PennCOSYVIO demonstrate that the point–line visual–inertial odometry system developed in this paper achieved significant improvements in both localization accuracy and efficiency compared with several state-of-the-art VIO systems.
Highlights
Simultaneous localization and mapping (SLAM) has become a key technology in autonomous driving and autonomous robot navigation, which has attracted widespread attention from academia and industry [1]
We compared the IPL–Visual–inertial odometry (VIO) proposed in this paper with OKVIS–Mono [9], VINS–Mono [10], and point–line visual–inertial odometry (PL–VIO) [24] to verify the effectiveness of the method
The Helmert variance component estimation method was introduced in the sliding window optimization, which ensured that more reasonable weights can be assigned for point features and line features
Summary
Simultaneous localization and mapping (SLAM) has become a key technology in autonomous driving and autonomous robot navigation, which has attracted widespread attention from academia and industry [1]. The earliest VIO systems are mainly based on filtering technology [4,5] by using the integral of inertial measurement unit (IMU) measurement information to predict the state variables of the motion carrier, which further updates the state variables with visual information, so as to realize the tightly coupled approaches of vision and IMU information. The OKVIS [9] system uses tightly coupled approaches to optimize the visual constraints of feature points and the preintegration constraints of IMU, and adopts optimization strategy based on keyframe and “first-in first-out” sliding window method by marginalizing the measurements from the oldest state. The VINS [10] system is a monocular visual–inertial SLAM scheme, which uses a sliding-window-based approach to construct the tightly coupled optimization of IMU preintegration and visual measurement information. The oldest frame and the latest frame are selectively marginalized to maintain the optimized state variables and achieve a good optimization effect
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