In order to improve the bit allocation efficiency of Directionlet coding,a modified Breiman,Friedman,Olshen and Stone(BFOS) algorithm is suggested for rate consistent truncation.Two modifications are:(1) Dominant dire...In order to improve the bit allocation efficiency of Directionlet coding,a modified Breiman,Friedman,Olshen and Stone(BFOS) algorithm is suggested for rate consistent truncation.Two modifications are:(1) Dominant direction adjustment is proposed to balance the cost of sparse description and segment artifacts caused by discontinuous adjacent direction pairs.(2) Priority related merging is also proposed in the BFOS distortion list to find an optimal trimming element for unequal-importance bit allocation.Experimental results show that block effects could be removed without obvious bpp increment by selecting the dominant direction and its adjustment according to neighborhood homogeneity,and combined multi-PRIority(PRI) based merging of the M-ordered list offers unequal importance allocation and leads to a Peak Signal-to-Noise Ratio(PSNR) gain of 0.4 dB～1.3 dB.展开更多
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
基金Supported by The National Natural Science Foundation of China (No.60972133)Guangzhou Natural Science Foundation Team Project (No.9351064101000003 & 8451008901000615)
文摘In order to improve the bit allocation efficiency of Directionlet coding,a modified Breiman,Friedman,Olshen and Stone(BFOS) algorithm is suggested for rate consistent truncation.Two modifications are:(1) Dominant direction adjustment is proposed to balance the cost of sparse description and segment artifacts caused by discontinuous adjacent direction pairs.(2) Priority related merging is also proposed in the BFOS distortion list to find an optimal trimming element for unequal-importance bit allocation.Experimental results show that block effects could be removed without obvious bpp increment by selecting the dominant direction and its adjustment according to neighborhood homogeneity,and combined multi-PRIority(PRI) based merging of the M-ordered list offers unequal importance allocation and leads to a Peak Signal-to-Noise Ratio(PSNR) gain of 0.4 dB～1.3 dB.
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）.
