| Simultaneous Localization and Mapping(SLAM)algorithm is a hot research direction in recent years.It has been widely used in autonomous robot detection in indoor and outdoor environments.The main application area of a SLAM-based mobile robot is Ambient Assisted Living(AAL).Mobile robots play an important role in AAL by supporting or guiding people with reduced mobility to move indoors.By using both Inertial Measurement Unit(IMU)and camera,VI-SLAM(Visual-Inertial SLAM)algorithm has become an important part of such robots.These AAL robots represent a typical situation where the robot is only involved in ground navigation tasks when moving on the ground.In response to this situation,this paper designs and implements a VI-SLAM algorithm for level ground navigation.The algorithm is improved and re-developed based on the open source algorithm ORBSLAM.It mainly selects the appropriate descriptors for the characteristics of level ground navigation and increases the processing of IMU information.In order to optimize the parameter configuration in the level ground navigation scene,we compare the performance of the three different feature descriptors in the motion environment for the moving characteristics of the AAL robot and find the descriptor that is most suitable for the condition.At the same time,because the SLAM algorithm of the monocular camera has an inevitable defect,that is,the scale is uncertain,and the IMU inertial navigation has drift problem,we have added the function of processing the IMU information,and using the tight coupling method to simultaneously use the visual image.Information and IMU information are positioned to ensure a relatively low cost of use while achieving a more accurate positioning effect.By running the EUROC datasets and comparing the estimated pose information,we can find that the VI-SLAM algorithm we developed has better positioning accuracy than the ORB-SLAM and VINS-mono,and is more suitable for level ground navigation. |
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