Reliable Vehicle Pose Estimation Using Vision and a Single-Track Model

Nilsson, Jonas; Fredriksson, Jonas; Odblom, Anders

doi:10.1109/tits.2014.2322196

Cited by 21 publications

(8 citation statements)

References 35 publications

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“…In robotic applications, pose estimation is often referred to as simultaneous localization and map building (SLAM) and has been extensively explored. SLAM has a history of adopting diverse sensor types and various motion models and a majority of the approaches have used recursive filtering techniques, such as the extended Kalman filter (EKF) [ 20 , 56 ], particle filter [ 57 ], unscented Kalman filter [ 58 ] and Kalman Filter. According to [ 20 , 25 , 27 , 59 ], EKF is the most appropriate technique to be adopted for inertial and visual fusion.…”

Section: Related Workmentioning

confidence: 99%

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

167

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Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

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Section: Related Workmentioning

confidence: 99%

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

167

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“…As shown in Fig. 3, the model ignores the vertical, pitch and roll motions, and the sideslip angles of the left wheels are assumed to be equal to those of right [31]. For generality, the model is considered under the condition of a small front steering angle.…”

Section: Predictive Plant and Dynamic Model A Predictive Plant Fmentioning

confidence: 99%

“…whereĤ L andĜ L are achieved by introducing (31) into (7) and (22), respectively. The transformation matrix in (32b) is defined as…”

Section: Solving Mathematical Problem With Exponential Weightmentioning

confidence: 99%

Electrical Vehicle Path Tracking Based Model Predictive Control With a Laguerre Function and Exponential Weight

et al. 2019

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Model predictive control (MPC) is advantageous for designing an electrical vehicle path-tracking controller, but the high computational complexity, mathematical problem, and parameterization challenge adversely affect the control performance. Hence, based on a fully actuated-by-wire electrical vehicle (FAW-EV), a novel path-tracking controller based on improved MPC with a Laguerre function and exponential weight (LEMPC) is designed. The massive optimization control parameters of MPC with a long control horizon are reduced by introducing a fitting orthogonal sequence consisting of Laguerre functions, thereby substantially reducing the computational complexity without sacrificing the tracking accuracy. An exponential weight with decreasing characteristic is introduced to MPC to solve the mathematical problem, thereby improving the robustness of the path tracking controller. In addition, the parameterization access for online adjusting path tracking control performance can be provided by the proposed method. The path tracking motion realization for FAW-EV is subsequently illustrated. Finally, several simulations are implemented to verify the advantages of the proposed method. INDEX TERMS Path track, electrical vehicle, model predictive control (MPC), Laguerre function, exponential weight.

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“…In this paper, we use a linear Kalman filter (LKF) for combining IMU sensor and camera position data. The LKF is simple and easy to implement for real time applications as compared to the extended Kalman filter (EKF) [110,111,112], particle filter [113] and unscented Kalman filter (UKF) [114].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Poulose

Han

2019

Sensors

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Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error.

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Reliable Vehicle Pose Estimation Using Vision and a Single-Track Model

Cited by 21 publications

References 35 publications

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Electrical Vehicle Path Tracking Based Model Predictive Control With a Laguerre Function and Exponential Weight

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

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