Gyro-aided camera-odometer online calibration and localization

Li, Dongxuan; Wu, Kanzhi; Wang, Yue; Xiong, Rong; Huang, Guoquan

doi:10.23919/acc.2017.7963501

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…Based on an analytical gyro preintegration factor, Li el al. [3] proposed a novel calibration system using factor graph, which estimates the camera-odometer extrinsic parameters. Based on [3], He et al [4] constructed a virtual measurement for the camera and built a new type of factor.…”

Section: Related Workmentioning

confidence: 99%

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A Two-Stage Perception System Calibration Algorithm for Quadruped Robots Using Factor Graph

Sun,

Wu,

Liu

et al. 2023

Advances in Transdisciplinary Engineering

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The accurate calibration of the perception system is one of the key prerequisites for quadruped robots to move safely and robustly in challenging environments. Not only does it provide relative transformations of the sensors w.r.t the robot body but also the time offset between unsynchronized sensors. This paper presents a two-stage spatio-temporal calibration framework for the body-eye calibration of quadruped robots. By attaching one complementary sensor, we first estimate the time offset between the sensor frame and body frame without complicated modifications to the mechanical system. Then we estimate the extrinsic transformation by formulating a hand-eye calibration problem while using the information from the kinematic model and encoder measurements. The extrinsic parameters are finally optimized by minimizing the re-projection error and the kinematics error in a factor graph formulation with a Lie group state representation. The experiments demonstrate that the proposed algorithm produces precise calibration results which guarantee high-accuracy environmental mapping using an RGB-D camera.

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Section: Related Workmentioning

confidence: 99%

“…[3] proposed a novel calibration system using factor graph, which estimates the camera-odometer extrinsic parameters. Based on [3], He et al [4] constructed a virtual measurement for the camera and built a new type of factor. The factor graph optimization allows to estimate the extrinsic parameter and the state of robot simultaneously.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Perception System Calibration Algorithm for Quadruped Robots Using Factor Graph

Sun,

Wu,

Liu

et al. 2023

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…To solve this problem, Wu et al (2017) makes the scale, pitch and roll observable by incorporating odometer measurements and planar motion constraints. Li et al (2017) presents a gyroaided camera-odometer online calibration and localization method, which is based on the stereo vision without the scale estimation and the initial calibration. Furthermore, Liu et al (2019) considers both gyroscope and accelerometer measurements in the preintegration and optimization, but fails to pay an attention to the significance of angular velocity of wheel encoder and the planar motion constraint.…”

Section: Related Workmentioning

confidence: 99%

Accurate and robust odometry by fusing monocular visual, inertial, and wheel encoder

Niu

Liu

Wang

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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Tracking the pose of a robot has been gaining importance in the field of Robotics, e.g., paving the way for robot navigation. In recent years, monocular visual-inertial odometry (VIO) is widely used to do the pose estimation due to its good performance and low cost. However, VIO cannot estimate the scale or orientation accurately when robots move along straight lines or circular arcs on the ground. To address the problem, in this paper we take the wheel encoder into account, which can provide us with stable translation information as well as small accumulated errors and momentary slippage errors. By jointly considering the kinematic constraints and the planar moving features, an odometry algorithm tightly coupled with monocular camera, IMU, and wheel encoder is proposed to get robust and accurate pose sensing for mobile robots, which mainly contains three steps. First, we present the wheel encoder preintegration theory and noise propagation formula based on the kinematic mobile robot model, which is the basis of accurate estimation in backend optimization. Second, we adopt a robust initialization method to obtain good initial values of gyroscope bias and visual scale in reality, by making full use of the camera, IMU and wheel encoder measurements. Third, we bound the high computation complexity with a marginalization strategy that conditionally eliminates unnecessary measurements in the sliding window. We implement a prototype and several extensive experiments showing that our system can achieve robust and accurate pose estimation, in terms of the scale, orientation and location, compared with the state-of-the-art.

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“…Wu et al [ 30 ] proposed fusing wheel speed encoder measurements into VIO based on the square-root inverse sliding window filter (SR-ISWF) [ 32 ], which significantly improved positioning accuracy under special motions of ground differential steering robots. Li et al [ 33 ] proposed a factor-graph-based, gyro-aided localization system by exploiting the wheel odometry and gyro measurements, which achieved better accuracy than ORB-SLAM [ 34 , 35 ]. KO-Fusion [ 36 ] was proposed to fuse the Mecanum wheel motion constraint into RGB-D SLAM for ground robots, which improved the robustness of SLAM.…”

Section: Introductionmentioning

confidence: 99%

Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization

Shi

et al. 2020

Sensors

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The observability of the scale direction in visual–inertial odometry (VIO) under degenerate motions of intelligent and connected vehicles can be improved by fusing Ackermann error state measurements. However, the relative kinematic error measurement model assumes that the vehicle velocity is constant between two consecutive camera states, which degrades the positioning accuracy. To address this problem, a consistent monocular Ackermann VIO, termed MAVIO, is proposed to combine the vehicle velocity and yaw angular rate error measurements, taking into account the lever arm effect between the vehicle and inertial measurement unit (IMU) coordinates with a tightly coupled filter-based mechanism. The lever arm effect is firstly introduced to improve the reliability for information exchange between the vehicle and IMU coordinates. Then, the process model and monocular visual measurement model are presented. Subsequently, the vehicle velocity and yaw angular rate error measurements are directly used to refine the estimator after visual observation. To obtain a global position for the vehicle, the raw Global Navigation Satellite System (GNSS) error measurement model, termed MAVIO-GNSS, is introduced to further improve the performance of MAVIO. The observability, consistency and positioning accuracy were comprehensively compared using real-world datasets. The experimental results demonstrated that MAVIO not only improved the observability of the VIO scale direction under the degenerate motions of ground vehicles, but also resolved the inconsistency problem of the relative kinematic error measurement model of the vehicle to further improve the positioning accuracy. Moreover, MAVIO-GNSS further improved the vehicle positioning accuracy under a long-distance driving state. The source code is publicly available for the benefit of the robotics community.

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Gyro-aided camera-odometer online calibration and localization

Cited by 12 publications

References 24 publications

A Two-Stage Perception System Calibration Algorithm for Quadruped Robots Using Factor Graph

A Two-Stage Perception System Calibration Algorithm for Quadruped Robots Using Factor Graph

Accurate and robust odometry by fusing monocular visual, inertial, and wheel encoder

Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization

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