Simultaneous localisation and mapping(SLAM)are the basis for many robotic applications.As the front end of SLAM,visual odometry is mainly used to estimate camera pose.In dynamic scenes,classical methods are deteriorat...Simultaneous localisation and mapping(SLAM)are the basis for many robotic applications.As the front end of SLAM,visual odometry is mainly used to estimate camera pose.In dynamic scenes,classical methods are deteriorated by dynamic objects and cannot achieve satisfactory results.In order to improve the robustness of visual odometry in dynamic scenes,this paper proposed a dynamic region detection method based on RGBD images.Firstly,all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint successively.Meanwhile,the depth image is clustered using the K-Means method.The classified feature points are mapped to the clustered depth image,and a dynamic or static label is assigned to each cluster according to the number of dynamic feature points.Subsequently,a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image,and the feature points covered by the mask are all removed.The remaining static feature points are applied to estimate the camera pose.Finally,some experimental results are provided to demonstrate the feasibility and performance.展开更多
Speech recognition(SR)systems based on deep neural networks are increasingly widespread in smart devices.However,they are vulnerable to human-imperceptible adversarial attacks,which cause the SR to generate incorrect ...Speech recognition(SR)systems based on deep neural networks are increasingly widespread in smart devices.However,they are vulnerable to human-imperceptible adversarial attacks,which cause the SR to generate incorrect or targeted adversarial commands.Meanwhile,audio adversarial attacks are particularly susceptible to various factors,e.g.,ambient noise,after applying them to a real-world attack.To circumvent this issue,we develop a universal adversarial perturbation(UAP)generation method to construct robust real-world UAP by integrating ambient noise into the generation process.The proposed UAP can work well in the case of input-agnostic and independent sources.We validate the effectiveness of our method on two different SRs in different real-world scenarios and parameters,the results demonstrate that our method yields state-of-the-art performance,i.e.given any audio waveform,the word error rate can be up to 80%.Extensive experiments investigate the impact of different parameters(e.g,signal-to-noise ratio,distance,and attack angle)on the attack success rate.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.U1913201,U22B2041)Natural Science Foundation of Liaoning Province(Grant No.2019-ZD-0169).
文摘Simultaneous localisation and mapping(SLAM)are the basis for many robotic applications.As the front end of SLAM,visual odometry is mainly used to estimate camera pose.In dynamic scenes,classical methods are deteriorated by dynamic objects and cannot achieve satisfactory results.In order to improve the robustness of visual odometry in dynamic scenes,this paper proposed a dynamic region detection method based on RGBD images.Firstly,all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint successively.Meanwhile,the depth image is clustered using the K-Means method.The classified feature points are mapped to the clustered depth image,and a dynamic or static label is assigned to each cluster according to the number of dynamic feature points.Subsequently,a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image,and the feature points covered by the mask are all removed.The remaining static feature points are applied to estimate the camera pose.Finally,some experimental results are provided to demonstrate the feasibility and performance.
文摘Speech recognition(SR)systems based on deep neural networks are increasingly widespread in smart devices.However,they are vulnerable to human-imperceptible adversarial attacks,which cause the SR to generate incorrect or targeted adversarial commands.Meanwhile,audio adversarial attacks are particularly susceptible to various factors,e.g.,ambient noise,after applying them to a real-world attack.To circumvent this issue,we develop a universal adversarial perturbation(UAP)generation method to construct robust real-world UAP by integrating ambient noise into the generation process.The proposed UAP can work well in the case of input-agnostic and independent sources.We validate the effectiveness of our method on two different SRs in different real-world scenarios and parameters,the results demonstrate that our method yields state-of-the-art performance,i.e.given any audio waveform,the word error rate can be up to 80%.Extensive experiments investigate the impact of different parameters(e.g,signal-to-noise ratio,distance,and attack angle)on the attack success rate.
