Address

Room 101, Institute of Cyber-Systems and Control, Yuquan Campus, Zhejiang University, Hangzhou, Zhejiang, China

Contact Information

Email: xingxingzuo@zju.edu.cn

Xingxing Zuo

PhD Student

Institute of Cyber-Systems and Control, Zhejiang University, China

Biography

I’m a PhD student working on SLAM, sensor fusion, visual-inertial navigation system, artificial intelligence. I start my PhD from September 2016 after getting my bachelor degree from the University of Electronic Science and Technology of China (UESTC). I’m co-advised by Prof. Yong Liu in ZJU and Prof. Guoquan Huang in the University of Delaware, America. Now I’m a visting Ph.D. student (2019-2021) in CVG group, ETHz, Switzerland.

Research and Interests

Robotics
Sensor Fusion
Artificial Intelligence

Publications

  • Xingxing Zuo, Wenlong Ye, Yulin Yang, Renjie Zheng, Teresa Vidal-Calleja, Guoquan Huang, and Yong Liu. Multimodal localization: Stereo over LiDAR map. Journal of Field Robotics, 37:1003–1026, 2020.
    [BibTeX] [Abstract] [PDF]
    In this paper, we present a real‐time high‐precision visual localization system for an autonomous vehicle which employs only low‐cost stereo cameras to localize the vehicle with a priori map built using a more expensive 3D LiDAR sensor. To this end, we construct two different visual maps: a sparse feature visual map for visual odometry (VO) based motion tracking, and a semidense visual map for registration with the prior LiDAR map. To register two point clouds sourced from different modalities (i.e., cameras and LiDAR), we leverage probabilistic weighted normal distributions transformation (ProW‐NDT), by particularly taking into account the uncertainty of source point clouds. The registration results are then fused via pose graph optimization to correct the VO drift. Moreover, surfels extracted from the prior LiDAR map are used to refine the sparse 3D visual features that will further improve VO‐based motion estimation. The proposed system has been tested extensively in both simulated and real‐world experiments, showing that robust, high‐precision, real‐time localization can be achieved.
    @article{zuo2020multimodalls,
    title = {Multimodal localization: Stereo over LiDAR map},
    author = {Xingxing Zuo and Wenlong Ye and Yulin Yang and Renjie Zheng and Teresa Vidal-Calleja and Guoquan Huang and Yong Liu},
    year = 2020,
    journal = {Journal of Field Robotics},
    volume = 37,
    pages = {1003--1026},
    abstract = {In this paper, we present a real‐time high‐precision visual localization system for an autonomous vehicle which employs only low‐cost stereo cameras to localize the vehicle with a priori map built using a more expensive 3D LiDAR sensor. To this end, we construct two different visual maps: a sparse feature visual map for visual odometry (VO) based motion tracking, and a semidense visual map for registration with the prior LiDAR map. To register two point clouds sourced from different modalities (i.e., cameras and LiDAR), we leverage probabilistic weighted normal distributions transformation (ProW‐NDT), by particularly taking into account the uncertainty of source point clouds. The registration results are then fused via pose graph optimization to correct the VO drift. Moreover, surfels extracted from the prior LiDAR map are used to refine the sparse 3D visual features that will further improve VO‐based motion estimation. The proposed system has been tested extensively in both simulated and real‐world experiments, showing that robust, high‐precision, real‐time localization can be achieved.}
    }
  • Mingming Zhang, Xingxing Zuo, Yiming Chen, Yong Liu, and Mingyang Li. Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization. IEEE Transactions on Robotics, 2021.
    [BibTeX] [Abstract] [DOI] [arXiv] [PDF]
    In this paper, we focus on motion estimation dedicated for non-holonomic ground robots, by probabilistically fusing measurements from the wheel odometer and exteroceptive sensors. For ground robots, the wheel odometer is widely used in pose estimation tasks, especially in applications under planar-scene based environments. However, since the wheel odometer only provides 2D motion estimates, it is extremely challenging to use that for performing accurate full 6D pose (3D position and 3D orientation) estimation. Traditional methods on 6D pose estimation either approximate sensor or motion models, at the cost of accuracy reduction, or rely on other sensors, e.g., inertial measurement unit (IMU), to provide complementary measurements. By contrast, in this paper, we propose a novel method to utilize the wheel odometer for 6D pose estimation, by modeling and utilizing motion manifold for ground robots. Our approach is probabilistically formulated and only requires the wheel odometer and an exteroceptive sensor (e.g., a camera). Specifically, our method i) formulates the motion manifold of ground robots by parametric representation, ii) performs manifold based 6D integration with the wheel odometer measurements only, and iii) re-parameterizes manifold equations periodically for error reduction. To demonstrate the effectiveness and applicability of the proposed algorithmic modules, we integrate that into a sliding-window pose estimator by using measurements from the wheel odometer and a monocular camera. By conducting extensive simulated and real-world experiments, we show that the proposed algorithm outperforms competing state-of-the-art algorithms by a significant margin in pose estimation accuracy, especially when deployed in complex large-scale real-world environments.
    @article{zhang2021poseef,
    title = {Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization},
    author = {Mingming Zhang and Xingxing Zuo and Yiming Chen and Yong Liu and Mingyang Li},
    year = 2021,
    journal = {IEEE Transactions on Robotics},
    doi = {10.1109/TRO.2020.3043970},
    abstract = {In this paper, we focus on motion estimation dedicated for non-holonomic ground robots, by probabilistically fusing measurements from the wheel odometer and exteroceptive sensors. For ground robots, the wheel odometer is widely used in pose estimation tasks, especially in applications under planar-scene based environments. However, since the wheel odometer only provides 2D motion estimates, it is extremely challenging to use that for performing accurate full 6D pose (3D position and 3D orientation) estimation. Traditional methods on 6D pose estimation either approximate sensor or motion models, at the cost of accuracy reduction, or rely on other sensors, e.g., inertial measurement unit (IMU), to provide complementary measurements. By contrast, in this paper, we propose a novel method to utilize the wheel odometer for 6D pose estimation, by modeling and utilizing motion manifold for ground robots. Our approach is probabilistically formulated and only requires the wheel odometer and an exteroceptive sensor (e.g., a camera). Specifically, our method i) formulates the motion manifold of ground robots by parametric representation, ii) performs manifold based 6D integration with the wheel odometer measurements only, and iii) re-parameterizes manifold equations periodically for error reduction. To demonstrate the effectiveness and applicability of the proposed algorithmic modules, we integrate that into a sliding-window pose estimator by using measurements from the wheel odometer and a monocular camera. By conducting extensive simulated and real-world experiments, we show that the proposed algorithm outperforms competing state-of-the-art algorithms by a significant margin in pose estimation accuracy, especially when deployed in complex large-scale real-world environments.},
    arxiv = {https://arxiv.org/pdf/1909.03423.pdf}
    }
  • Jiajun Lv, Kewei Hu, Jinhong Xu, Yong Liu, and Xingxing Zuo. CLINS: Continuous-Time Trajectory Estimation for LiDAR Inertial System. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021.
    [BibTeX]
    @inproceedings{lv2021cct,
    title = {CLINS: Continuous-Time Trajectory Estimation for LiDAR Inertial System},
    author = {Jiajun Lv and Kewei Hu and Jinhong Xu and Yong Liu and Xingxing Zuo},
    year = 2021,
    booktitle = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems}
    }
  • Xingxing Zuo, Nathaniel Merrill, Wei Li, Yong Liu, Marc Pollefeys, and Guoquan (Paul) Huang. CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth. In 2021 IEEE International Conference on Robotics and Automation, 2021.
    [BibTeX] [arXiv]
    @inproceedings{zuo2021cvi,
    title = {CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth},
    author = {Xingxing Zuo and Nathaniel Merrill and Wei Li and Yong Liu and Marc Pollefeys and Guoquan (Paul) Huang},
    year = 2021,
    booktitle = {2021 IEEE International Conference on Robotics and Automation},
    arxiv = {https://arxiv.org/abs/2012.10133}
    }
  • Jiajun Lv, Jinhong Xu, Kewei Hu, Yong Liu, and Xingxing Zuo. Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), page 9968–9975, 2020.
    [BibTeX] [Abstract] [DOI] [arXiv] [PDF]
    Sensor calibration is the fundamental block for a multi-sensor fusion system. This paper presents an accurate and repeatable LiDAR-IMU calibration method (termed LI-Calib), to calibrate the 6-DOF extrinsic transformation between the 3D LiDAR and the Inertial Measurement Unit (IMU). Regarding the high data capture rate for LiDAR and IMU sensors, LI-Calib adopts a continuous-time trajectory formulation based on B-Spline, which is more suitable for fusing high-rate or asynchronous measurements than discrete-time based approaches. Additionally, LI-Calib decomposes the space into cells and identifies the planar segments for data association, which renders the calibration problem well-constrained in usual scenarios without any artificial targets. We validate the proposed calibration approach on both simulated and real-world experiments. The results demonstrate the high accuracy and good repeatability of the proposed method in common human-made scenarios. To benefit the research community, we open-source our code at https://github.com/APRIL-ZJU/lidar_IMU_calib.
    @inproceedings{lv2020targetlessco,
    title = {Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation},
    author = {Jiajun Lv and Jinhong Xu and Kewei Hu and Yong Liu and Xingxing Zuo},
    year = 2020,
    booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    pages = {9968--9975},
    doi = {https://doi.org/10.1109/IROS45743.2020.9341405},
    abstract = {Sensor calibration is the fundamental block for a multi-sensor fusion system. This paper presents an accurate and repeatable LiDAR-IMU calibration method (termed LI-Calib), to calibrate the 6-DOF extrinsic transformation between the 3D LiDAR and the Inertial Measurement Unit (IMU). Regarding the high data capture rate for LiDAR and IMU sensors, LI-Calib adopts a continuous-time trajectory formulation based on B-Spline, which is more suitable for fusing high-rate or asynchronous measurements than discrete-time based approaches. Additionally, LI-Calib decomposes the space into cells and identifies the planar segments for data association, which renders the calibration problem well-constrained in usual scenarios without any artificial targets. We validate the proposed calibration approach on both simulated and real-world experiments. The results demonstrate the high accuracy and good repeatability of the proposed method in common human-made scenarios. To benefit the research community, we open-source our code at https://github.com/APRIL-ZJU/lidar_IMU_calib.},
    arxiv = {https://arxiv.org/pdf/2007.14759.pdf}
    }
  • Xingxing Zuo, Yulin Yang, Patrick Geneva, Jiajun Lv, Yong Liu, Guoquan Huang, and Marc Pollefeys. LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), page 5112–5119, 2020.
    [BibTeX] [Abstract] [arXiv] [PDF]
    Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robotics and beyond. In this paper, building upon our prior work (i.e., LIC-Fusion), we develop a sliding-window filter based LiDAR-Inertial-Camera odometry with online spatiotemporal calibration (i.e., LIC-Fusion 2.0), which introduces a novel sliding-window plane-feature tracking for efficiently processing 3D LiDAR point clouds. In particular, after motion compensation for LiDAR points by leveraging IMU data, low-curvature planar points are extracted and tracked across the sliding window. A novel outlier rejection criteria is proposed in the plane-feature tracking for high quality data association. Only the tracked planar points belonging to the same plane will be used for plane initialization, which makes the plane extraction efficient and robust. Moreover, we perform the observability analysis for the IMU-LiDAR subsystem under consideration and report the degenerate cases for spatiotemporal calibration using plane features. While the estimation consistency and identified degenerate motions are validated in Monte-Carlo simulations, different real-world experiments are also conducted to show that the proposed LIC-Fusion 2.0 outperforms its predecessor and other state-of-the-art methods.
    @inproceedings{zuo2020licfusion2l,
    title = {LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking},
    author = {Xingxing Zuo and Yulin Yang and Patrick Geneva and Jiajun Lv and Yong Liu and Guoquan Huang and Marc Pollefeys},
    year = 2020,
    booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    pages = {5112--5119},
    abstract = {Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robotics and beyond. In this paper, building upon our prior work (i.e., LIC-Fusion), we develop a sliding-window filter based LiDAR-Inertial-Camera odometry with online spatiotemporal calibration (i.e., LIC-Fusion 2.0), which introduces a novel sliding-window plane-feature tracking for efficiently processing 3D LiDAR point clouds. In particular, after motion compensation for LiDAR points by leveraging IMU data, low-curvature planar points are extracted and tracked across the sliding window. A novel outlier rejection criteria is proposed in the plane-feature tracking for high quality data association. Only the tracked planar points belonging to the same plane will be used for plane initialization, which makes the plane extraction efficient and robust. Moreover, we perform the observability analysis for the IMU-LiDAR subsystem under consideration and report the degenerate cases for spatiotemporal calibration using plane features. While the estimation consistency and identified degenerate motions are validated in Monte-Carlo simulations, different real-world experiments are also conducted to show that the proposed LIC-Fusion 2.0 outperforms its predecessor and other state-of-the-art methods.},
    arxiv = {https://arxiv.org/pdf/2008.07196.pdf}
    }
  • Yulin Yang, Patrick Geneva, Xingxing Zuo, Kevin Eckenhoff, Yong Liu, and Guoquan Huang. Tightly-Coupled Aided Inertial Navigation with Point and Plane Features. In 2019 International Conference on Robotics and Automation (ICRA), page 6094–6100, 2019.
    [BibTeX] [Abstract] [DOI] [PDF]
    This paper presents a tightly-coupled aided inertial navigation system (INS) with point and plane features, a general sensor fusion framework applicable to any visual and depth sensor (e.g., RGBD, LiDAR) configuration, in which the camera is used for point feature tracking and depth sensor for plane extraction. The proposed system exploits geometrical structures (planes) of the environments and adopts the closest point (CP) for plane parameterization. Moreover, we distinguish planar point features from non-planar point features in order to enforce point-on-plane constraints which are used in our state estimator, thus further exploiting structural information from the environment. We also introduce a simple but effective plane feature initialization algorithm for feature-based simultaneous localization and mapping (SLAM). In addition, we perform online spatial calibration between the IMU and the depth sensor as it is difficult to obtain this critical calibration parameter in high precision. Both Monte-Carlo simulations and real-world experiments are performed to validate the proposed approach.
    @inproceedings{yang2019tightlycoupledai,
    title = {Tightly-Coupled Aided Inertial Navigation with Point and Plane Features},
    author = {Yulin Yang and Patrick Geneva and Xingxing Zuo and Kevin Eckenhoff and Yong Liu and Guoquan Huang},
    year = 2019,
    booktitle = {2019 International Conference on Robotics and Automation (ICRA)},
    pages = {6094--6100},
    doi = {https://doi.org/10.1109/ICRA.2019.8794078},
    abstract = {This paper presents a tightly-coupled aided inertial navigation system (INS) with point and plane features, a general sensor fusion framework applicable to any visual and depth sensor (e.g., RGBD, LiDAR) configuration, in which the camera is used for point feature tracking and depth sensor for plane extraction. The proposed system exploits geometrical structures (planes) of the environments and adopts the closest point (CP) for plane parameterization. Moreover, we distinguish planar point features from non-planar point features in order to enforce point-on-plane constraints which are used in our state estimator, thus further exploiting structural information from the environment. We also introduce a simple but effective plane feature initialization algorithm for feature-based simultaneous localization and mapping (SLAM). In addition, we perform online spatial calibration between the IMU and the depth sensor as it is difficult to obtain this critical calibration parameter in high precision. Both Monte-Carlo simulations and real-world experiments are performed to validate the proposed approach.}
    }
  • Xingxing Zuo, Patrick Geneva, Woosik Lee, Yong Liu, and Guoquan Huang. LIC-Fusion: LiDAR-Inertial-Camera Odometry. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), page 5848–5854, 2019.
    [BibTeX] [Abstract] [DOI] [arXiv] [PDF]
    This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points. In particular, the proposed LIC-Fusion performs online spatial and temporal sensor calibration between all three asynchronous sensors, in order to compensate for possible calibration variations. The key contribution is the optimal (up to linearization errors) multi-modal sensor fusion of detected and tracked sparse edge/surf feature points from LiDAR scans within an efficient MSCKF-based framework, alongside sparse visual feature observations and IMU readings. We perform extensive experiments in both indoor and outdoor environments, showing that the proposed LIC-Fusion outperforms the state-of-the-art visual-inertial odometry (VIO) and LiDAR odometry methods in terms of estimation accuracy and robustness to aggressive motions.
    @inproceedings{zuo2019licfusionlo,
    title = {LIC-Fusion: LiDAR-Inertial-Camera Odometry},
    author = {Xingxing Zuo and Patrick Geneva and Woosik Lee and Yong Liu and Guoquan Huang},
    year = 2019,
    booktitle = {2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    pages = {5848--5854},
    doi = {https://doi.org/10.1109/IROS40897.2019.8967746},
    abstract = {This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points. In particular, the proposed LIC-Fusion performs online spatial and temporal sensor calibration between all three asynchronous sensors, in order to compensate for possible calibration variations. The key contribution is the optimal (up to linearization errors) multi-modal sensor fusion of detected and tracked sparse edge/surf feature points from LiDAR scans within an efficient MSCKF-based framework, alongside sparse visual feature observations and IMU readings. We perform extensive experiments in both indoor and outdoor environments, showing that the proposed LIC-Fusion outperforms the state-of-the-art visual-inertial odometry (VIO) and LiDAR odometry methods in terms of estimation accuracy and robustness to aggressive motions.},
    arxiv = {http://arxiv.org/pdf/1909.04102}
    }
  • Xingxing Zuo, Mingming Zhang, Yiming Chen, Yong Liu, Guoquan Huang, and Mingyang Li. Visual-Inertial Localization for Skid-Steering Robots with Kinematic Constraints. In 2019 The International Symposium on Robotics Research (ISRR), 2019.
    [BibTeX] [Abstract] [arXiv] [PDF]
    While visual localization or SLAM has witnessed great progress in past decades, when deploying it on a mobile robot in practice, few works have explicitly considered the kinematic (or dynamic) constraints of the real robotic system when designing state estimators. To promote the practical deployment of current state-of-the-art visual-inertial localization algorithms, in this work we propose a low-cost kinematics-constrained localization system particularly for a skid-steering mobile robot. In particular, we derive in a principle way the robot’s kinematic constraints based on the instantaneous centers of rotation (ICR) model and integrate them in a tightly-coupled manner into the sliding-window bundle adjustment (BA)-based visual-inertial estimator. Because the ICR model parameters are time-varying due to, for example, track-to-terrain interaction and terrain roughness, we estimate these kinematic parameters online along with the navigation state. To this end, we perform in-depth the observability analysis and identify motion conditions under which the state/parameter estimation is viable. The proposed kinematics-constrained visual-inertial localization system has been validated extensively in different terrain scenarios.
    @inproceedings{zuo2019visualinertiallf,
    title = {Visual-Inertial Localization for Skid-Steering Robots with Kinematic Constraints},
    author = {Xingxing Zuo and Mingming Zhang and Yiming Chen and Yong Liu and Guoquan Huang and Mingyang Li},
    year = 2019,
    booktitle = {2019 The International Symposium on Robotics Research (ISRR)},
    abstract = {While visual localization or SLAM has witnessed great progress in past decades, when deploying it on a mobile robot in practice, few works have explicitly considered the kinematic (or dynamic) constraints of the real robotic system when designing state estimators. To promote the practical deployment of current state-of-the-art visual-inertial localization algorithms, in this work we propose a low-cost kinematics-constrained localization system particularly for a skid-steering mobile robot. In particular, we derive in a principle way the robot's kinematic constraints based on the instantaneous centers of rotation (ICR) model and integrate them in a tightly-coupled manner into the sliding-window bundle adjustment (BA)-based visual-inertial estimator. Because the ICR model parameters are time-varying due to, for example, track-to-terrain interaction and terrain roughness, we estimate these kinematic parameters online along with the navigation state. To this end, we perform in-depth the observability analysis and identify motion conditions under which the state/parameter estimation is viable. The proposed kinematics-constrained visual-inertial localization system has been validated extensively in different terrain scenarios.},
    arxiv = {https://arxiv.org/pdf/1911.05787.pdf}
    }
  • Xingxing Zuo, Patrick Geneva, Yulin Yang, Wenlong Ye, Yong Liu, and Guoquan Huang. Visual-Inertial Localization With Prior LiDAR Map Constraints. IEEE Robotics and Automation Letters, 4:3394–3401, 2019.
    [BibTeX] [Abstract] [DOI] [PDF]
    In this letter, we develop a low-cost stereo visual-inertial localization system, which leverages efficient multi-state constraint Kalman filter (MSCKF)-based visual-inertial odometry (VIO) while utilizing an a priori LiDAR map to provide bounded-error three-dimensional navigation. Besides the standard sparse visual feature measurements used in VIO, the global registrations of visual semi-dense clouds to the prior LiDAR map are also exploited in a tightly-coupled MSCKF update, thus correcting accumulated drift. This cross-modality constraint between visual and LiDAR pointclouds is particularly addressed. The proposed approach is validated on both Monte Carlo simulations and real-world experiments, showing that LiDAR map constraints between clouds created through different sensing modalities greatly improve the standard VIO and provide bounded-error performance.
    @article{zuo2019visualinertiallw,
    title = {Visual-Inertial Localization With Prior LiDAR Map Constraints},
    author = {Xingxing Zuo and Patrick Geneva and Yulin Yang and Wenlong Ye and Yong Liu and Guoquan Huang},
    year = 2019,
    journal = {IEEE Robotics and Automation Letters},
    volume = 4,
    pages = {3394--3401},
    doi = {https://doi.org/10.1109/LRA.2019.2927123},
    abstract = {In this letter, we develop a low-cost stereo visual-inertial localization system, which leverages efficient multi-state constraint Kalman filter (MSCKF)-based visual-inertial odometry (VIO) while utilizing an a priori LiDAR map to provide bounded-error three-dimensional navigation. Besides the standard sparse visual feature measurements used in VIO, the global registrations of visual semi-dense clouds to the prior LiDAR map are also exploited in a tightly-coupled MSCKF update, thus correcting accumulated drift. This cross-modality constraint between visual and LiDAR pointclouds is particularly addressed. The proposed approach is validated on both Monte Carlo simulations and real-world experiments, showing that LiDAR map constraints between clouds created through different sensing modalities greatly improve the standard VIO and provide bounded-error performance.}
    }
  • Xingxing Zuo, Xiaojia Xie, Yong Liu, and Guoquan Huang. Robust visual SLAM with point and line features. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), page 1775–1782, 2017.
    [BibTeX] [Abstract] [DOI] [arXiv] [PDF]
    In this paper, we develop a robust efficient visual SLAM system that utilizes heterogeneous point and line features. By leveraging ORB-SLAM [1], the proposed system consists of stereo matching, frame tracking, local mapping, loop detection, and bundle adjustment of both point and line features. In particular, as the main theoretical contributions of this paper, we, for the first time, employ the orthonormal representation as the minimal parameterization to model line features along with point features in visual SLAM and analytically derive the Jacobians of the re-projection errors with respect to the line parameters, which significantly improves the SLAM solution. The proposed SLAM has been extensively tested in both synthetic and real-world experiments whose results demonstrate that the proposed system outperforms the state-of-the-art methods in various scenarios.
    @inproceedings{zuo2017robustvs,
    title = {Robust visual SLAM with point and line features},
    author = {Xingxing Zuo and Xiaojia Xie and Yong Liu and Guoquan Huang},
    year = 2017,
    booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    pages = {1775--1782},
    doi = {https://doi.org/10.1109/IROS.2017.8205991},
    arxiv = {http://arxiv.org/pdf/1711.08654},
    abstract = {In this paper, we develop a robust efficient visual SLAM system that utilizes heterogeneous point and line features. By leveraging ORB-SLAM [1], the proposed system consists of stereo matching, frame tracking, local mapping, loop detection, and bundle adjustment of both point and line features. In particular, as the main theoretical contributions of this paper, we, for the first time, employ the orthonormal representation as the minimal parameterization to model line features along with point features in visual SLAM and analytically derive the Jacobians of the re-projection errors with respect to the line parameters, which significantly improves the SLAM solution. The proposed SLAM has been extensively tested in both synthetic and real-world experiments whose results demonstrate that the proposed system outperforms the state-of-the-art methods in various scenarios.}
    }