Multi-Stage Matching Approach for Mobile Platform Visual Imagery

Self Cite

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.

“…. Q n }, and the correlation coefficient of single pixel in the matching line is calculated using Formula (1) [34]. The respective correlation coefficients between P i and {Q 1 , Q 2 , .…”

Section: Improved Line Feature Matching Algorithmmentioning

confidence: 99%

Improved Point–Line Visual–Inertial Odometry System Using Helmert Variance Component Estimation

Zhang

et al. 2020

Self Cite

“…It enables the robot's positional estimation and scene construction in unknown environments by analyzing its onboard sensors. Visual SLAM [5] uses the camera as the primary sensor, which has the advantages of low cost, easy installation, and rich environmental information. Due to the increasing amount of attention from scholars in the past few decades, many VSLAM systems with good performance have been developed, such as LSD-SLAM [6], DSO [7], and ORB-SLAM2 [8].…”

Section: Introductionmentioning

confidence: 99%

DGS-SLAM: A Fast and Robust RGBD SLAM in Dynamic Environments Combined by Geometric and Semantic Information

Long

et al. 2022

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Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the static world assumption, which limits their application in real dynamic scenarios. To improve the robustness and efficiency of the system in dynamic environments, this paper proposes a dynamic RGBD SLAM based on a combination of geometric and semantic information (DGS-SLAM). First, a dynamic object detection module based on the multinomial residual model is proposed, which executes the motion segmentation of the scene by combining the motion residual information of adjacent frames and the potential motion information of the semantic segmentation module. Second, a camera pose tracking strategy using feature point classification results is designed to achieve robust system tracking. Finally, according to the results of dynamic segmentation and camera tracking, a semantic segmentation module based on a semantic frame selection strategy is designed for extracting potential moving targets in the scene. Extensive evaluation in public TUM and Bonn datasets demonstrates that DGS-SLAM has higher robustness and speed than state-of-the-art dynamic RGB-D SLAM systems in dynamic scenes.

“…However, with the development of society, the demand for intelligent UAVs is increasing. How to make UAVs operate safely and quickly in complex environments has become a research hotspot in the world [11,12]. Some researchers have studied the path planning algorithm of UAVs to ensure they fly under the interference of external environmental factors [13,14], and some researchers have proposed some path planning algorithms for the case of hardware failure and other emergencies [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Robust and Efficient Trajectory Replanning Based on Guiding Path for Quadrotor Fast Autonomous Flight

Zhao

Chen

et al. 2021

Self Cite

Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in an unknown cluttered environment. However, real-time and stability remain a significant challenge in the field of path planning. To improve the robustness and efficiency of the path planning method in complex environments, this paper presents RETRBG, a robust and efficient trajectory replanning method based on the guiding path. Firstly, a safe guiding path is generated by using an improved A* and path pruning method, which is used to perceive the narrow space in its surrounding environment. Secondly, under the guidance of the path, a guided kinodynamic path searching method (GKPS) is devised to generate a safe, kinodynamically feasible and minimum-time initial path. Finally, an adaptive optimization function with two modes is proposed to improve the optimization quality in complex environments, which selects the optimization mode to optimize the smoothness and safety of the path according to the perception results of the guiding path. The experimental results demonstrate that the proposed method achieved good performance both in different obstacle densities and different resolutions. Compared with the other state-of-the-art methods, the quality and success rate of the planning result are significantly improved.