RESUMEN
Most visual simultaneous localization and mapping (SLAM) systems are based on the assumption of a static environment in autonomous vehicles. However, when dynamic objects, particularly vehicles, occupy a large portion of the image, the localization accuracy of the system decreases significantly. To mitigate this challenge, this paper unveils DOT-SLAM, a novel stereo visual SLAM system that integrates dynamic object tracking through graph optimization. By integrating dynamic object pose estimation into the SLAM system, the system can effectively utilize both foreground and background points for ego vehicle localization and obtain a static feature points map. To rectify the inaccuracies in depth estimation from stereo disparity directly on the foreground points of dynamic objects due to their self-similarity characteristics, a coarse-to-fine depth estimation method based on camera-road plane geometry is presented. This method uses rough depth to guide fine stereo matching, thereby obtaining the 3 dimensions (3D)spatial positions of feature points on dynamic objects. Subsequently, by establishing constraints on the dynamic object's pose using the road plane and non-holonomic constraints (NHCs) of the vehicle, reducing the initial pose uncertainty of dynamic objects leads to more accurate dynamic object initialization. Finally, by considering foreground points, background points, the local road plane, the ego vehicle pose, and dynamic object poses as optimization nodes, through the establishment and joint optimization of a nonlinear model based on graph optimization, accurate six degrees of freedom (DoFs) pose estimations are obtained for both the ego vehicle and dynamic objects. Experimental validation on the KITTI-360 dataset demonstrates that DOT-SLAM effectively utilizes features from the background and dynamic objects in the environment, resulting in more accurate vehicle trajectory estimation and a static environment map. Results obtained from a real-world dataset test reinforce the effectiveness.
RESUMEN
Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six DoF motion in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.
RESUMEN
In this work, more than 70 wt % of ferromanganese slag (containing 40 wt % CaO) was used to synthesize high-calcium, CaO-MgO-Al2O3-SiO2 (CMAS) glass ceramics. The effect of SiO2/CaO on the structure, crystallization behavior and microstructure of high-calcium, CMAS, slag glass ceramics was studied by IR, NMR, DSC, XRD and SEM. The results showed that in the high-calcium, CMAS glass ceramics, the main existing forms of silicon-oxygen tetrahedra (Qn) were Q0 and Q1. With the increase in the SiO2/CaO, Qn changed from Q0 and Q1 (main units) to Q1 (main units) and Q2, and then to Q1 and Q2 (main units). The polymerization degree of Qn changed from low to high, making the glass more stable, which led to the increase in crystallization temperature and the decrease in crystallization kinetic constant (k) and frequency factor (υ). At the same time, the change in the Qn structure resulted in a gradual change to the main crystal, from akermanite to diopside-wollastonite.