No abstract

Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile robot. For this reason, this paper proposes an adaptive model predictive control (MPC) with an accurate localization fluctuation assessment for mobile robots, which balances the contradiction between precision and calculation efficiency of mobile robot control. The distinctive features of the proposed MPC are three-fold: (1) Integrating variance and entropy—a localization fluctuation estimation relying on fuzzy logic rules is proposed to enhance the accuracy of the fluctuation assessment. (2) By using the Taylor expansion-based linearization method—a modified kinematics model that considers that the external disturbance of localization fluctuation is established to satisfy the iterative solution of the MPC method and reduce the computational burden. (3) An improved MPC with an adaptive adjustment of predictive step size according to localization fluctuation is proposed, which alleviates the disadvantage of a large amount of the MPC calculation and improves the stability of the control system in dynamic scenes. Finally, verification experiments of the real-life mobile robot are offered to verify the effectiveness of the presented MPC method. Additionally, compared with PID, the tracking distance and angle error of the proposed method decrease by 74.3% and 95.3%, respectively.

Detection of parking slots occupation is a crucial task for parking assistance, automatic parking, and autonomous driving systems. This paper proposed a novel method, called Temporal Difference of Inverse Perspective Mapping Difference (TD-IPM), without explicit 3D reconstruction or objection detection. In this method, temporal images from monocular camera are first inverse perspective mapped (IPM) onto the ground plane based on camera calibration results. Second, we proposed an algorithm, called Block Consensus based on Rotation Invariance Phase-Only Correlation (BC-RIPOC), for fast and robust motion estimation. From the estimated motion, we can align these two IPM images and generate IPM difference map. Third, the IPM difference map is segmented and filtered to generate a binary map that can distinguish objects on the ground plane or not for occupation detection. The obstacle is readily localized from the difference map as well. The proposed TD-IPM method has been validated in both underground and outdoor parking lots. Experimental results demonstrate that the proposed TD-IPM method can successfully detect various occupation objects, such as vehicles, cones, lockers, and others, with 97.9% average detection accuracy and speed of 17.5 frames per second (fps). The proposed method suggests an effective and low-cost solution to intelligent parking systems.

Accurate and robust self-localization is a crucial task for intelligent vehicles. Because of limited access to GPS signals, localization in underground parking lots remains a problem. In this paper, fusion localization for intelligent vehicles using the widely available around view monitoring (AVM) is conducted by Kalman filter based on second-order Markov motion model (KF-MM2). The proposed method consists of two steps, one for visual map construction from AVM images and the other for map-based multi-scale localization. The proposed visual map consists of a series of nodes. Each node encodes both holistic and local visual features computed from AVM images, three-dimensional structure, and vehicle pose. In the localization step, the process of image-level localization is modeled as a Hidden Markov Model (HMM), in which the map nodes are hidden states. The result of image-level localization is calculated using forward algorithm by the given AVM image sequence. Then the metric localization is computed from local features matching. Finally, the metric localization is fused with the prediction by KF-MM2. The proposed method has been verified in two typical underground parking lots. Experimental results demonstrate that the proposed method can achieve an average error of 0.39 m in underground parking lots.

This paper proposes a new method of target localization and tracking. The method consists of four parts. The first part is to divide the scene into multiple cells based on the camera’s parameters and calibrate the position and error of each vertex. The second part mainly uses the bounding box detection algorithm, YOLOv4, based on deep learning to detect and recognize the scene image sequence and obtain the type, length, width, and position of the target to be tracked. The third part is to match each vertex of the cell in the image and the cell in the scene, generate a homography matrix, and then use the PnP model to calculate the precise world coordinates of the target in the image. In this process, a cell-based accuracy positioning method is proposed for the first time. The fourth part uses the proposed PTH model to convert the obtained world coordinates into P, T, and H values for the purpose of actively tracking and observing the target in the scene with a PTZ camera. The proposed method achieved precise target positioning and tracking in a 50 cm ∗ 250 cm horizontal channel and a vertical channel. The experimental results show that the method can accurately identify the target to be tracked in the scene, can actively track the moving target in the observation scene, and can obtain a clear image and accurate trajectory of the target. It is verified that the maximum positioning error of the proposed cell-based positioning method is 2.31 cm, and the average positioning error is 1.245 cm. The maximum error of the proposed tracking method based on the PTZ camera is 1.78 degrees, and the average error is 0.656 degrees.