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  • 1.
    Tang, Jiexiong
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Deep Learning Assisted Visual Odometry2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The capabilities to autonomously explore and interact with the environmenthas always been a greatly demanded capability for robots. Varioussensor based SLAM methods were investigated and served for this purposein the past decades. Vision intuitively provides 3D understanding of the surroundingand contains a vast amount of information that require high levelintelligence to interpret. Sensors like LIDAR, returns the range measurementdirectly. The motion estimation and scene reconstruction using camera is aharder problem. In this thesis, we are in particular interested in the trackingfrond-end of vision based SLAM, i.e. Visual Odometry (VO), with afocus on deep learning approaches. Recently, learning based methods havedominated most of the vision applications and gradually appears in our dailylife and real-world applications. Different to classical methods, deep learningbased methods can potentially tackle some of the intrinsic problems inmulti-view geometry and straightforwardly improve the performance of crucialprocedures of VO. For example, the correspondences estimation, densereconstruction and semantic representation.

    In this work, we propose novel learning schemes for assisting both directand in-direct visual odometry methods. For the direct approaches, weinvestigate mainly the monocular setup. The lack of the baseline that providesscale as in stereo has been one of the well-known intrinsic problems inthis case. We propose a coupled single view depth and normal estimationmethod to reduce the scale drift and address the issue of lacking observationsof the absolute scale. It is achieved by providing priors for the depthoptimization. Moreover, we utilize higher-order geometrical information toguide the dense reconstruction in a sparse-to-dense manner. For the in-directmethods, we propose novel feature learning based methods which noticeablyimprove the feature matching performance in comparison with common classicalfeature detectors and descriptors. Finally, we discuss potential ways tomake the training self-supervised. This is accomplished by incorporating thedifferential motion estimation into the training while performing multi-viewadaptation to maximize the repeatability and matching performance. We alsoinvestigate using a different type of supervisory signal for the training. Weadd a higher-level proxy task and show that it is possible to train a featureextraction network even without the explicit loss for it.

    In summary, this thesis presents successful examples of incorporating deeplearning techniques to assist a classical visual odometry system. The resultsare promising and have been extensively evaluated on challenging benchmarks,real robot and handheld cameras. The problem we investigate is stillin an early stage, but is attracting more and more interest from researcher inrelated fields.

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  • 2.
    Tang, Jiexiong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Ericson, Ludvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    GCNv2: Efficient Correspondence Prediction for Real-Time SLAM2019In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 4, p. 3505-3512Article in journal (Refereed)
    Abstract [en]

    In this letter, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORBSLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone. Source code is available at: https://github.com/jiexiong2016/GCNv2_SLAM.

  • 3.
    Tang, Jiexiong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Sparse2Dense: From Direct Sparse Odometry to Dense 3-D Reconstruction2019In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 2, p. 530-537Article in journal (Refereed)
    Abstract [en]

    In this letter, we proposed a new deep learning based dense monocular simultaneous localization and mapping (SLAM) method. Compared to existing methods, the proposed framework constructs a dense three-dimensional (3-D) model via a sparse to dense mapping using learned surface normals. With single view learned depth estimation as prior for monocular visual odometry, we obtain both accurate positioning and high-quality depth reconstruction. The depth and normal are predicted by a single network trained in a tightly coupled manner. Experimental results show that our method significantly improves the performance of visual tracking and depth prediction in comparison to the state-of-the-art in deep monocular dense SLAM.

  • 4.
    Tang, Jiexiong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Hanme, Kim
    Vitor, Guizilini
    Sudeep, Pillai
    Rares, Ambrus.
    Neural Outlier Rejection for Self-Supervised KeypointLearning2020In: International Conference on Learning Representations(ICLR), Apr 26th through May 1st, 2020, 2020Conference paper (Refereed)
  • 5.
    Tang, Jiexiong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Rares, Ambrus
    Vitor, Guizilini
    Sudeep, Pillai
    Hanme, Kim
    Adrien, Gaidon
    Self-Supervised 3D Keypoint Learning for Ego-motionEstimation2019Report (Other academic)
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