In this paper,we present a novel algorithm for odometry estimation based on ceiling vision.The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error acc...In this paper,we present a novel algorithm for odometry estimation based on ceiling vision.The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error accumulation problem in most visual odometry estimation approaches.The principal direction is defned based on the fact that our ceiling is flled with artifcial vertical and horizontal lines which can be used as reference for the current robot s heading direction.The proposed approach can be operated in real-time and it performs well even with camera s disturbance.A moving low-cost RGB-D camera（Kinect）,mounted on a robot,is used to continuously acquire point clouds.Iterative closest point（ICP） is the common way to estimate the current camera position by registering the currently captured point cloud to the previous one.However,its performance sufers from data association problem or it requires pre-alignment information.The performance of the proposed principal direction detection approach does not rely on data association knowledge.Using this method,two point clouds are properly pre-aligned.Hence,we can use ICP to fne-tune the transformation parameters and minimize registration error.Experimental results demonstrate the performance and stability of the proposed system under disturbance in real-time.Several indoor tests are carried out to show that the proposed visual odometry estimation method can help to signifcantly improve the accuracy of simultaneous localization and mapping（SLAM）.展开更多
Multiple sources localization is an important technique applied in many areasl such as sonar, radar, biomedical, and geology exploration. High resolution localization needs to employ jointly two sorts of high resoluti...Multiple sources localization is an important technique applied in many areasl such as sonar, radar, biomedical, and geology exploration. High resolution localization needs to employ jointly two sorts of high resolution estimators, time-delay estimator and Direction Of Arrival (DOA) estimator, both of which are tough problems attracting many signal processing researchers. There is also another difficulty that is to pair two groups of parameters in timedelay and DOA domains. In underwater environment, multiple sources localization research faces more difficulties because of the long duration emitted wave, limit aperture of array, and short data record of echoes. In this paper, an new extended ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) method is presented. With a single echo wave, both time-delay and DOA parameters of multiple sources are estimated simultaneously.No additional pairing algorithm is needed to obtain the source locations. The performance of the new estimators and the probability of correct pairing is given by computer simulations, andthe results shows that good estimation can be obtained in low SNR (Signal to Noise Ratio) for multiple sources localization.展开更多
文摘In this paper,we present a novel algorithm for odometry estimation based on ceiling vision.The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error accumulation problem in most visual odometry estimation approaches.The principal direction is defned based on the fact that our ceiling is flled with artifcial vertical and horizontal lines which can be used as reference for the current robot s heading direction.The proposed approach can be operated in real-time and it performs well even with camera s disturbance.A moving low-cost RGB-D camera（Kinect）,mounted on a robot,is used to continuously acquire point clouds.Iterative closest point（ICP） is the common way to estimate the current camera position by registering the currently captured point cloud to the previous one.However,its performance sufers from data association problem or it requires pre-alignment information.The performance of the proposed principal direction detection approach does not rely on data association knowledge.Using this method,two point clouds are properly pre-aligned.Hence,we can use ICP to fne-tune the transformation parameters and minimize registration error.Experimental results demonstrate the performance and stability of the proposed system under disturbance in real-time.Several indoor tests are carried out to show that the proposed visual odometry estimation method can help to signifcantly improve the accuracy of simultaneous localization and mapping（SLAM）.
文摘Multiple sources localization is an important technique applied in many areasl such as sonar, radar, biomedical, and geology exploration. High resolution localization needs to employ jointly two sorts of high resolution estimators, time-delay estimator and Direction Of Arrival (DOA) estimator, both of which are tough problems attracting many signal processing researchers. There is also another difficulty that is to pair two groups of parameters in timedelay and DOA domains. In underwater environment, multiple sources localization research faces more difficulties because of the long duration emitted wave, limit aperture of array, and short data record of echoes. In this paper, an new extended ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) method is presented. With a single echo wave, both time-delay and DOA parameters of multiple sources are estimated simultaneously.No additional pairing algorithm is needed to obtain the source locations. The performance of the new estimators and the probability of correct pairing is given by computer simulations, andthe results shows that good estimation can be obtained in low SNR (Signal to Noise Ratio) for multiple sources localization.
