Vibrations often cause visual fatigue for drivers, and measuring the relative motion between the driver and the display is important for evaluating this visual fatigue. This paper proposes a non-contact videometric measurement method for studying the three-dimensional trajectories of the driver’s eyes based on stereo vision. The feasibility of this method is demonstrated by dynamic calibration. A high-speed dual-camera image acquisition system is used to obtain high-definition images of the face, and the relative trajectories between the eyes and the display are obtained by a set of robust algorithms. The trajectories of the eyes in three-dimensional space are then reconstructed during the vehicle driving process. This new approach provides three-dimensional information and is effective for assessing how vibration affects human visual performance.

Tracking drift issues resulting from the lack of global positioning constraints prevent visual simultaneous localization and mapping (SLAM) systems from providing accurate pose estimation services, especially in indoor environments. Similar to the function of the Global Navigation Satellite System (GNSS), a new indoor equipment called workshop Measurement Positioning System (wMPS) has been developed to provide millimeter-level global positioning information in the large-volume metrology industrial community but is rarely applied to SLAM solutions. In this paper, we introduce a novel wMPS-SLAM framework that combines 3 degree-of-freedom (DoF) wMPS signals into our monocular pose estimation algorithm, enabling drift-free global 6DoF pose estimation in indoor scenarios. Firstly, a system calibration method is proposed to establish the relationship between the camera and wMPS and fuse the sensor information. Secondly, a variant similarity transformation scheme is employed to jointly estimate the scale factor and extrinsic parameters of the camera frame w.r.t the world frame for estimating the global camera poses. Lastly, a pose graph optimization method that minimizes global position and relative pose errors is utilized to fuse Visual Odometry (VO) estimation and wMPS measurements. Evaluation experiments conducted on two datasets demonstrate that wMPS-SLAM outperforms the state-of-the-art monocular visual solution (ORB-SLAM3) in terms of accuracy. Specifically, the absolute translation error (ATE) of the proposed system is reduced from 28.58mm to 7.018mm in real-world experiments.

This paper presents Open-Structure, a novel benchmark dataset for evaluating visual odometry and SLAM methods. Compared to existing public datasets that primarily offer raw images, Open-Structure provides direct access to point and line measurements, correspondences, structural associations, and co-visibility factor graphs, which can be fed to various stages of SLAM pipelines to mitigate the impact of data preprocessing modules in ablation experiments. The dataset comprises two distinct types of sequences from the perspective of scenarios. The first type maintains reasonable observation and occlusion relationships, as these critical elements are extracted from public image-based sequences using our dataset generator. In contrast, the second type consists of carefully designed simulation sequences that enhance dataset diversity by introducing a wide range of trajectories and observations. Furthermore, a baseline is proposed using our dataset to evaluate widely used modules, including camera pose tracking, parametrization, and factor graph optimization, within SLAM systems. By evaluating these state-of-the-art algorithms across different scenarios, we discern each module's strengths and weaknesses in the context of camera tracking and optimization processes. The Open-Structure dataset and baseline system are openly accessible on website: https://open-structure.github.io , encouraging further research and development in the field of SLAM.

Coded mark point, as an important part of automatic matching, is widely used in stereo vision and photogrammetry. An encoding scheme named the Initial Dot encoding scheme, has been designed based on the programme of concentric rings, and which is able to solve restrictions on the number of codes and promote the stability of decoding. The designed coding pattern includes three functional points: locating point, start point and coded point. The maximum coding number of the Initial Dot scheme can reach 262,144 when the number of code bits is 18. The recognition experiments show that the Initial Dot encoding scheme can achieve 100% recognition rate at a 75 degree angle, which is very difficult for other coding schemes. By comparing experiments with the Schneider encoding scheme for a large flexible panel, the recognition rate of the proposed scheme is 99%, which is higher than the recognition rate of 90% of the Schneider scheme. The experimental results indicate that the Initial Dot encoding scheme can be successfully applied on flexible solar panels under a 1000 mm × 500 mm field of view.

Based on wMPS (workshop Measurement Positioning System) and visual sensors, the joint localization and mapping system (wMPS-Point) has demonstrated significant enhancements in positioning accuracy compared to conventional visual SLAM, which employs 3DoF point measurements of tags (wMPS receivers) obtained from multiple anchors (wMPS transmitters ≥ 2) [1]. However, when the anchors encounter occlusion or failure, the 2DoF bearing-only observations from a single anchor become insufficient to recover the 3D position of the tag attached to the camera sensor using the triangulation principle, resulting in a significant decrease in the localization accuracy. To address the challenge, this paper proposes a general robust positioning approach that integrates 2DoF bearing measurements into visual-based camera tracking and optimization modules. First, the 2D bearing measurement is represented as the angle parameterization to establish a residual edge connecting camera poses and fixed wMPS anchor. Furthermore, a tightly coupled optimization framework is designed to take advantage of both visual and bearing measurements to achieve more accurate camera pose estimation performance. To ensure algorithmic robustness, an outlier filtering method is also presented to cope with anomalous data in wMPS bearing-only measurements. The effectiveness of the proposed method was evaluated using open-source datasets and real-life experiments. The results indicate that the proposed method outperforms wMPS-Point in the consistency of multiple sequences localization results. Even when our method leverages bearing-only measurements from a single anchor, its localization accuracy is comparable to that achieved using multiple wMPS anchors.