PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment

In indoor low-texture environments, the point feature-based visual SLAM system has poor robustness and low trajectory accuracy. Therefore, we propose a visual inertial SLAM algorithm based on point-line feature fusion. Firstly, in order to improve the quality of the extracted line segment, a line segment extraction algorithm with adaptive threshold value is proposed. By constructing the adjacent matrix of the line segment and judging the direction of the line segment, it can decide whether to merge or eliminate other line segments. At the same time, geometric constraint line feature matching is considered to improve the efficiency of processing line features. Compared with the traditional algorithm, the processing efficiency of our proposed method is greatly improved. Then, point, line, and inertial data are effectively fused in a sliding window to achieve high-accuracy pose estimation. Finally, experiments on the EuRoC dataset show that the proposed PLI-VINS performs better than the traditional visual inertial SLAM system using point features and point line features.

A Tightly Coupled Visual-Inertial GNSS State Estimator Based on Point-Line Feature

Visual-inertial odometry (VIO) is known to suffer from drifting and can only provide local coordinates. In this paper, we propose a tightly coupled GNSS-VIO system based on point-line features for robust and drift-free state estimation. Feature-based methods are not robust in complex areas such as weak or repeated textures. To deal with this problem, line features with more environmental structure information can be extracted. In addition, to eliminate the accumulated drift of VIO, we tightly fused the GNSS measurement with visual and inertial information. The GNSS pseudorange measurements are real-time and unambiguous but experience large errors. The GNSS carrier phase measurements can achieve centimeter-level positioning accuracy, but the solution to the whole-cycle ambiguity is complex and time-consuming, which degrades the real-time performance of a state estimator. To combine the advantages of the two measurements, we use the carrier phase smoothed pseudorange instead of pseudorange to perform state estimation. Furthermore, the existence of the GNSS receiver and IMU also makes the extrinsic parameter calibration crucial. Our proposed system can calibrate the extrinsic translation parameter between the GNSS receiver and IMU in real-time. Finally, we show that the states represented in the ECEF frame are fully observable, and the tightly coupled GNSS-VIO state estimator is consistent. We conducted experiments on public datasets. The experimental results demonstrate that the positioning precision of our system is improved and the system is robust and real-time.

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.