基于语义物体尺寸链的改进自适应蒙特卡洛动态定位研究

Dynamic Localization Research on Improved AMCL Based on the Dimensional Chain of Semantic Objects

为解决原始自适应蒙特卡洛定位(Adaptive Monte Carlo localization,AMCL)在相似动态环境下极易重定位失败的问题,首先融合机器人里程计信息、激光信息以及视觉信息,基于Gmapping算法完成机器人的即时定位与栅格地图的创建,其次将KinectV2彩色图像输入到目标检测识别方法中获取图像中物体检测框与类别,并结合GrabCut算法实现语义分割,利用配准数据计算出彩色图像中的物体在深度图像中对应位置,然后将获取到的KinectV2深度数据转换成点云信息并投影转换至全局地图坐标系下,得到物体语义图,并充分发掘各语义物体之间的关系,构建语义物体尺寸链,并提出一种快速有效的尺寸链检索方法,同时采用贝叶斯方法来减小误检测和重复检测对物体语义图的影响,将物体语义图和栅格地图进行原点重合以及位姿对齐,从而构建二维语义栅格地图,提出一种基于语义物体尺寸链的改进AMCL重定位算法,最后通过大量真实环境下的重定位实验验证了所提方法在相似动态环境中的优越性能。

The aim is to solve the problem that the original adaptive Monte Carlo localization is prone to re-localization failure in similar dynamic environment.Firstly,the odometer,laser radar and vision information are fused,and the instant localization of the robot and the creation of grid map are completed based on the Gmapping algorithm.Secondly,the KinectV2 color image is input into the object detection and recognition method to obtain the boxes and categories of object detection in the image,and the semantic segmentation is realized by combining GrabCut algorithm.The corresponding position of the object in the depth image is calculated by using the registration data.Then,the acquired KinectV2 depth data is converted into point cloud information and projected onto the global map coordinate system to obtain the object semantic map.The relationship between the semantic objects is fully explored,and the dimension chain of semantic objects is constructed.And a fast and effective retrieval method of dimension chain is proposed.At the same time,the Bayesian method is used to reduce the influence of false detection and repeated detection on object semantic map.The semantic map of object and grid map are aligned by origin coincidence and pose alignment,so as to construct two-dimensional semantic grid map.The research proposes an improved AMCL re-localization algorithm base on the dimension chain of semantic objects.Finally,a large number of re-localization experiments in real environments verify the superior performance of the method in similar and dynamic environments.