RESUMO
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.
RESUMO
Global navigation satellite system (GNSS)/inertial navigation system (INS) navigation technology is one of the core technologies in a mobile measurement system and can provide real-time geo-referenced information. However, in the environment measurements, buildings cover up the GNSS signal, causing satellite signals to experience loss-of-lock. At this time errors of INS independent navigation accumulate rapidly, so it cannot meet the needs of the mobile measurement system. In this paper, a positioning error compensation method based on plane control is proposed by analyzing the error characteristics of position and orientation caused by satellite signal loss-of-lock in the urban environment. This method control uses planar features and the laser point cloud positioning equation to establish an adjustment model that ignores the influence of the attitude error and finds the positioning error at the middle point of the GNSS signal loss-of-lock time period, and then compensates for the error at other points by using the characteristics of the positioning error. The experimental results show that the accuracy of the compensated laser point cloud has been significantly improved, and the feasibility of the method is verified. Meanwhile, the method can rely on the existing building plane so the method is adaptable and easy to implement.
RESUMO
In a group of general geometric primitives, plane-based features are widely used for indoor localization because of their robustness against noises. However, a lack of linearly independent planes may lead to a non-trivial estimation. This in return can cause a degenerate state from which all states cannot be estimated. To solve this problem, this paper first proposed a degeneracy detection method. A compensation method that could fix orientations by projecting an inertial measurement unit's (IMU) information was then explained. Experiments were conducted using an IMU-Kinect v2 integrated sensor system prone to fall into degenerate cases owing to its narrow field-of-view. Results showed that the proposed framework could enhance map accuracy by successful detection and compensation of degenerated orientations.