Objective

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The traditional SLAM system requires a camera to return to the position where tracking is lost to restart location and mapping

thereby greatly limiting the application of monocular SLAM scenarios. Thus

a new and highly adaptive recovery strategy is required. In a traditional keyframe-based SLAM system

relocalization is achieved by finding the keyframes (a reference camera pose and image) that match the current frame to estimate the camera pose. Localization only to previously visited places where keyframes were added to the map is possible. In practical application

relocalization is not a user-friendly solution because users prefer to walk into previous unseen areas where no associated keyframes exist. Moreover

finding matches between the two frames is difficult when the scene changes considerably. Tracking resumption can even be difficult when the camera arrives at the place from where it has been. The motion and map message during lost time cannot be recovered

which leads to an imperfect map. Thus

this study proposes a strategy of monocular vision combined with inertial measurement unit (IMU) to recover a lost map based on map fusion.

Method

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The ORB_SLAM system incorporates three threads that run in parallel:tracking

local mapping

and loop closing. Our algorithm improved on the basis of ORB_SLAM. When tracking fails in conditions with motion blur or occlusions

our system immediately restarts a new map. The first map created before tracking lost is saved

and relocalization is achieved by fusing the two maps. A new map is created by re-initializing. Compared with relocalization

initialization is achievable

whereas the scale of the camera trajectory computed by ORB_SLAM is arbitrary. The inconsistent scale must be solved first to fuse the two maps. The absolute scale of the map is calculated by combining monocular vision with IMU. The IMU sensor can recover the metric scale for monocular vision and is affected by the limitation of vision. The position of the camera is calculated by IMU data before the initialization is completed. The pose information during lost time can be used as a bridge to fuse the two maps when the new map is initialized. The process of map fusion consists of three steps:1) Coordinate transformation. The coordinate system of the new map has changed after re-initialization. The transformation between the new map and the lost map can be computed by IMU during the lost time

which is applied to the lost map to move the lost map onto the new map. 2) Matching strategy. The data measured by IMU contains various errors unavoidably affect the result of the map fusion. Thus

vision information is considered to eliminate errors. A jumping matching search strategy is proposed according to the covisibility relationship between keyframes to reduce matching time. We match keyframes selected from the two maps according to a certain condition instead of matching them individually. Thus

a considerable amount of matching time can be reduced. 3) Estimating the error between the two maps. After a set of matching map points is obtained

motion estimation between the matching points is solved by nonlinear optimization

which is applied to the two maps to reduce errors. Finally

the new map points in the matching map points are erased

and the two maps are merged. The relationship of the keyframes and the map points between the old and new maps is established

which are used to jointly optimize the pose of the camera and the position of the map points in subsequent tracking and mapping.

Result

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The algorithm is tested on an open dataset (visual-inertial datasets collected onboard a micro aerial vehicle). Results show that our method can effectively solve the system's inability to keep the map and location when SLAM system tracking fails. The experiment includes map accuracy and map completeness. When referring to accuracy

the trajectory of the fusion map achieves a typical precision of 8 cm for a 30 m

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room environment and a 300 m

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industrial environment compared with the ground truth. We compare fusion trajectory with the trajectory without loss and find that the precision can reach 4 cm. In case the camera moves violently

the map recovered by our algorithm is more complete than that by the ORB_SLAM relocalization algorithm. The result of our algorithm contains 30% more keyframes than the ORB_SLAM algorithm.

Conclusion

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The proposed algorithm can retrieve tracking by merging maps if tracking is lost and recover the trajectory during the lost time. Compared with the traditional SLAM system

our relocalization strategy does not need the camera to return to the previous place visited but only requires one to observe part of the place before the loss. Our algorithm is more adaptive than the ORB_SLAM algorithm in case the tracking fails soon after initialization.