Mapping in the dynamic environment is an important task for autonomous mobile robots due to the unavoidable changes in the workspace. In this paper, we propose a framework for RGBD SLAM in low dynamic environment, whi...Mapping in the dynamic environment is an important task for autonomous mobile robots due to the unavoidable changes in the workspace. In this paper, we propose a framework for RGBD SLAM in low dynamic environment, which can maintain a map keeping track of the latest environment. The main model describing the environment is a multi-session pose graph, which evolves over the multiple visits of the robot. The poses in the graph will be pruned when the 3D point scans corresponding to those poses are out of date. When the robot explores the new areas, its poses will be added to the graph. Thus the scans kept in the current graph will always give a map of the latest environment. The changes of the environment are detected by out-of-dated scans identification module through analyzing scans collected at different sessions. Besides, a redundant scans identification module is employed to further reduce the poses with redundant scans in order to keep the total number of poses in the graph with respect to the size of environment. In the experiments, the framework is first tuned and tested on data acquired by a Kinect from laboratory environment. Then the framework is applied to external dataset acquired by a Kinect II from a workspace of an industrial robot in another country, which is blind to the development phase, for further validation of the performance. After this two-step evaluation, the proposed framework is considered to be able to manage the map in date in dynamic or static environment with a noncumulative complexity and acceptable error level.展开更多
针对多层感知机(MLP)架构无法捕获会话序列上下文中的共现关系的问题,提出了一种基于图共现增强MLP的会话推荐模型GCE-MLP。首先,利用MLP架构捕获会话序列的顺序依赖关系,同时通过共现关系学习层获得序列上下文中的共现关系,并通过信息...针对多层感知机(MLP)架构无法捕获会话序列上下文中的共现关系的问题,提出了一种基于图共现增强MLP的会话推荐模型GCE-MLP。首先,利用MLP架构捕获会话序列的顺序依赖关系,同时通过共现关系学习层获得序列上下文中的共现关系,并通过信息融合模块得到会话表示;其次,设计了特定的特征选择层,旨在扩大不同关系学习层输入特征的差异性;最后,通过噪声对比任务最大化两种关系表征之间的互信息,进一步增强对会话兴趣的表征学习。在多个真实数据集上的实验结果表明GCE-MLP的推荐性能优于目前主流的模型,验证了该模型的有效性。与最优的MLP架构模型FMLP-Rec(Filter-enhanced MLP for Recommendation)相比,在Diginetica数据集上,P@20最高达到了54.08%,MRR@20最高达到了18.87%,分别提升了2.14和1.43个百分点;在Yoochoose数据集上,P@20最高达到了71.77%,MRR@20最高达到了31.78%,分别提升了0.48和1.77个百分点。展开更多
基金This work is supported by the National Natural Science Foundation of China （Grant No. NSFC： 61473258, U 1509210）, and the Joint Centre for Robotics Research （JCRR） between Zhejiang University and the University of Technology, Sydney.
文摘Mapping in the dynamic environment is an important task for autonomous mobile robots due to the unavoidable changes in the workspace. In this paper, we propose a framework for RGBD SLAM in low dynamic environment, which can maintain a map keeping track of the latest environment. The main model describing the environment is a multi-session pose graph, which evolves over the multiple visits of the robot. The poses in the graph will be pruned when the 3D point scans corresponding to those poses are out of date. When the robot explores the new areas, its poses will be added to the graph. Thus the scans kept in the current graph will always give a map of the latest environment. The changes of the environment are detected by out-of-dated scans identification module through analyzing scans collected at different sessions. Besides, a redundant scans identification module is employed to further reduce the poses with redundant scans in order to keep the total number of poses in the graph with respect to the size of environment. In the experiments, the framework is first tuned and tested on data acquired by a Kinect from laboratory environment. Then the framework is applied to external dataset acquired by a Kinect II from a workspace of an industrial robot in another country, which is blind to the development phase, for further validation of the performance. After this two-step evaluation, the proposed framework is considered to be able to manage the map in date in dynamic or static environment with a noncumulative complexity and acceptable error level.
文摘针对多层感知机(MLP)架构无法捕获会话序列上下文中的共现关系的问题,提出了一种基于图共现增强MLP的会话推荐模型GCE-MLP。首先,利用MLP架构捕获会话序列的顺序依赖关系,同时通过共现关系学习层获得序列上下文中的共现关系,并通过信息融合模块得到会话表示;其次,设计了特定的特征选择层,旨在扩大不同关系学习层输入特征的差异性;最后,通过噪声对比任务最大化两种关系表征之间的互信息,进一步增强对会话兴趣的表征学习。在多个真实数据集上的实验结果表明GCE-MLP的推荐性能优于目前主流的模型,验证了该模型的有效性。与最优的MLP架构模型FMLP-Rec(Filter-enhanced MLP for Recommendation)相比,在Diginetica数据集上,P@20最高达到了54.08%,MRR@20最高达到了18.87%,分别提升了2.14和1.43个百分点;在Yoochoose数据集上,P@20最高达到了71.77%,MRR@20最高达到了31.78%,分别提升了0.48和1.77个百分点。
