Development Of Mobile-device Assisted Dso(madso)for Robust And High-precision Robot Mapping

Direct Sparse Odometry(DSO)is a novel and high precision sparse odometry method of the direct SLAM.DSO uses probabilistic models and joint model parameter optimization methods to minimize photometric errors with faster processing speeds and higher accuracy.DSO has a good performance in pose localization and semi-dense mapping based on datasets.However,in real-world applications,DSO needs to improve its high sensitivity to illumination and photometric conditions as well as motion blurring.Moreover,DSO has no loop closing module,so it cannot effectively reduce the accumulated errors,and hence its robustness and accuracy have a large room for improvement.Based on the above analyses,this paper proposes a direct synchronous localization and mapping method assisted by using a mobile device to improve the accuracy and robustness of DSO,i.e.,MADSO.The noveltys or advantages of MADSO are as follows.This work can provide observation information for robot initialization and mapping by tracking robot with mobile device to effectively alleviate the problem of DSO scale drift in real-world applications.MADSO developped solutions for three typical situations when the mobile device tracks the robot for different applications to optimize the error of pose estimation.Meanwhile,we developped a trajectory smoothing algorithm to improve the odometry's accuracy when the mobile device loses the track of the robot.The major contributions of this work include: Analyzing the advantages and disadvantages of DSO,comparing the possible methods to improve the accuracy and the robustness of DSO,and performing experiments to reveal the problems of DSO in real-world applications,such as scale drift and illumination sensitivity.Developing a framework to estimate the pose of a tagged robot using a mobile device,whose pose is estimated with a lightweight SLAM scheme,which can help initialize the DSO based robot mapping.Setting up an experiment platform to verify the performance of MADSO with the help of a motion capture system.The experimental results show that,compared with DSO,LDSO and ORB-SLAM,the proposed MADSO method can achieve a great deal of improvement in terms of the trajectory estimation error and mapping accuracy under varying illumination and photometric conditions.