Objective

2

Simultaneous localization and mapping (SLAM) has been an important research topic in the last two decades in the computer vision and robotics communities. SLAM aims to build a map of an unknown environment and localize the sensor in the map with real-time operation. Among the existing research results

many state-of-the-art monocular visual SLAM schemes are used. In most existing traditional approaches

the system would set the initial frame instead of the absolute position as the reference frame when it begins running

and it could not acquire the pose in a fixed coordinate system

resulting in the failure of re-using the existing mapping information. Additionally

the present monocular visual SLAM schemes are prone to tracking failure in case of complicated scenes such as cluttered background

motion blur

and defocus

and the existing schemes process it by place recognition

which is a key module of a SLAM system to close loops and relocalize the camera. However

the main drawback of the solution is that it requires the current frame to be highly similar to the existing key frame to ensure the success of relocalization

generally causing inconvenience in certain real application scenarios. For example

an advanced mobile robot is required to return to the place where the location information is lost after tracking failure so that the system could continue tracking and mapping. However

the map information could not be recovered from the failure of system tracking to the success of relocalization

thereby resulting in considerable map information loss. Therefore

to address the limitations

such as loss of map information and requirement for high similarity between a current frame and an existing frame while relocalizing

we propose a prior-information-based visual SLAM system with the capacity of re-initialization

map re-using

and map recovery in complex scenes.

Method

2

In this study

the first step is loading the prior map

matching the current frame with the key frame of the prior map in the SLAM system by applying ORB features to obtain matched frames

and finishing the initialization of SLAM system in combination of relocalization

which is conducive to ensuring the consistency of the SLAM coordinate system. Second

a map-saving mechanism is built to save the map of successful tracking before tracing failure occurs to avoid losing the map information. The traditional SLAM always conducts relocalization after tracking failure

and the map could not be generated before the relocalization succeeds accordingly

further resulting in the loss of map information. Considering the aforementioned issues

we address tracking failure by re-initializing rather than relocalizing to establish a new map called the recovery map. To improve the probability of successful initialization and the ability to recover map information

we investigate a self-adaptation fast re-initialization algorithm with the introduction of vanishing point detection. Initially

the scene vanishing points are detected by M-estimator sample consensus (MSAC) method. If vanishing points are present

the fast initialization method would be used. Otherwise

a simple initialization method would be applied; in other words

the proposed algorithm reduces the initialization requirements of the traditional SLAM system. The optimal re-initialization strategy could be selected automatically to ensure that the SLAM system continues tracking and mapping. Finally

for the successful tracking map and the recovery map

the improved loop method is used to obtain the transformation relationship between them. Once the overlapping maps are detected

the relative position and pose relationships between overlapping maps is calculated. Furthermore

the recovery map is transformed into a coordinate system that is consistent with the coordinate system of the matched map in memory. Second

scale information is used to fuse overlapping map points

and a map recovery method is proposed to reduce the errors caused by different scales between the successful tracking map and the recovered map

thereby solving the discrepancy between two maps caused by different scales to obtain accurate global consistency.

Result

2

We have compared the proposed system with the state-of-the-art system ORB-SLAM2 on two public datasets

namely

KITTI and EuroC

and a dataset is recorded in complex scenarios. The evaluation metrics contain the number of key frames

integrity rate of key frames

and recovery rate of key frames; moreover

we provide several point cloud maps and trajectory maps of the two systems for comparison. The experimental results show that the proposed system not only performs comparably in accuracy with ORB-SLAM2

but also significantly outperforms state-of-the-art existing methods in various real-world settings in tracking and mapping robustness. The comparative experiment consists of three parts

and the final experiment results are used to verify the effectiveness of the proposed system. In the first part

the KITTI dataset is added with noise

causing the traditional SLAM system to fail in tracking. The map restoration ability of the proposed system on KITTI00

KITTI02

and KITTI05 increased by 39.25%

47.75%

and 32.46%

respectively. The experimental results show that the advantages of the proposed system are used to address tracking failure effectively and build a complete map

and the proposed systems are better than the state-of-the-art system ORB-SLAM2. In the second part

compared with the results in EuRoC

the proposed system has the same mapping accuracy on V1_01_easy dataset and V1_02_medium dataset as that of ORB-SLAM2. ORB-SLAM2 could not run steadily on the V1_03_difficult dataset

whereas the proposed system could

thereby also indicating that tracking stability has been significantly improved. The third part is the test in the real application scenario. The running dataset is obtained during walking more than one closed track in the underground garage with a camera. The underground garage has a small unlit room. The experimental results show that the proposed system still performs well in real application scenarios with environmental and motion complexity

and they are more complete than the map obtained by ORB-SLAM2.

Conclusion

2

Experimental results indicate that the proposed system can effectively recover maps in case of tracking failure. Furthermore

the proposed system can efficiently reuse the existing mapping results of the SLAM system

fix the map coordinate system

and significantly improve the robustness of the system.