Efficient integration of inertial observations into visual SLAM without initialization

Lupton, Todd; Sukkarieh, Salah

doi:10.1109/iros.2009.5354267

Cited by 33 publications

(30 citation statements)

References 4 publications

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“…A tactical-grade Honeywell HG1700A58 IMU was simulated, providing position, velocity and attitude constraints through the integration of the body rotation rates and accelerations (incorporating IMU constraints was not undertaken in Stanway's implementation). The method used to incorporate the inertial measurements into the filter is based on Lupton and Sukkarieh (2009) and Lupton (2010). A global reference frame is used, and initial attitude is assumed accurate for linearization purposes.…”

Section: Six Degrees-of-freedom Simulationmentioning

confidence: 99%

“…A global reference frame is used, and initial attitude is assumed accurate for linearization purposes. Compensation for Earth rotation (significant for heading estimation during long missions), as calculated in Titterton and Weston (2004), is achieved with the following equation, which is modified from Algorithm 1, Line 9 in Lupton and Sukkarieh (2009): (15) where −C k n Ω n e is the apparent Earth rotation in the body frame. The gyro and accelerometer bias states are augmented into the filter.…”

Section: Six Degrees-of-freedom Simulationmentioning

confidence: 99%

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Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

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Survey-class Autonomous Underwater Vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the midwater column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler (ADCP) mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.

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Section: Six Degrees-of-freedom Simulationmentioning

confidence: 99%

Section: Six Degrees-of-freedom Simulationmentioning

confidence: 99%

Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

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“…The loosely-coupled approach treats the inertial and vision units as two separate filters running at different rates and exchanging information, while the tightlycoupled approach combines them into a single, statistical filter. Among the loosely-coupled approaches are the works of [8], [9], [10], [11], [12], [13], [14], [15], [16], while among the tightly-coupled ones are those of [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27].…”

Section: Introductionmentioning

confidence: 99%

“…Of special interest to us are the works of [17], [23], [24], [25], [26], [27], [15], [16], since they use only a monocular vision-inertial system without known 3D points or any additional sensors. Strelow and Singh [17] implemented a tightly-coupled approach by combining the inertial readings into an EKF-based monocular SLAM.…”

Section: Introductionmentioning

confidence: 99%

“…Their implementation is similar to the popular monocular SLAM of Davison et al [4], but they replaced the constant velocity motion model by a centripetal acceleration motion model including the inertial readings as updating measurements. In [23], [24], Lupton and Sukkarieh implemented a filter to estimate both the absolute scale of monocular SLAM and the biases of the IMU. Similarly, Kelly and Sukatme [25], [26] demonstrated that a monocular EKF-SLAM approach may also be used for simultaneous camera-to-IMU calibration.…”

Section: Introductionmentioning

confidence: 99%

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Deterministic initialization of metric state estimation filters for loosely-coupled monocular vision-inertial systems

Kneip

Weiß

Siegwart

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this work, we present a novel, deterministic closed-form solution for computing the scale factor and the gravity direction of a moving, loosely-coupled, and monocular vision-inertial system. The methodology is based on analysing delta-velocities. On one hand, they are obtained from a differentiation of the up-to-scale camera pose computation by a visual odometry or visual SLAM algorithm. On the other hand, they can also be retrieved from the gravity-affected short-term integration of acceleration signals. We derive a method for separating the gravity contribution and recovering the metric scale factor of the vision algorithm. The method thus also recovers the offset in roll and pitch angles of the vision reference frame with respect to the direction of the gravity vector. It uses only a single inertial integration period, and no absolute orientation information is required. For optimal sensor-fusion and metric scale-estimation filters in the loosely-coupled case, it has been shown that the convergence of the fusion of an upto-scale pose information with inertial measurements largely depends on the availability of a good initial value for the scale factor. We show how this problem can be tackled by applying the method presented in this paper. Finally, we present results in simulation and on real data, demonstrating the suitability of the method in real scenarios.

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Monocular Vision for Long‐term Micro Aerial Vehicle State Estimation: A Compendium

Weiß

Achtelik

Lynen

et al. 2013

Journal of Field Robotics

224

162

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The recent technological advances in Micro Aerial Vehicles (MAVs) have triggered great interest in the robotics community, as their deployability in missions of surveillance and reconnaissance has now become a realistic prospect. The state of the art, however, still lacks solutions that can work for a long duration in large, unknown, and GPS‐denied environments. Here, we present our visual pipeline and MAV state‐estimation framework, which uses feeds from a monocular camera and an Inertial Measurement Unit (IMU) to achieve real‐time and onboard autonomous flight in general and realistic scenarios. The challenge lies in dealing with the power and weight restrictions onboard a MAV while providing the robustness necessary in real and long‐term missions. This article provides a concise summary of our work on achieving the first onboard vision‐based power‐on‐and‐go system for autonomous MAV flights. We discuss our insights on the lessons learned throughout the different stages of this research, from the conception of the idea to the thorough theoretical analysis of the proposed framework and, finally, the real‐world implementation and deployment. Looking into the onboard estimation of monocular visual odometry, the sensor fusion strategy, the state estimation and self‐calibration of the system, and finally some implementation issues, the reader is guided through the different modules comprising our framework. The validity and power of this framework are illustrated via a comprehensive set of experiments in a large outdoor mission, demonstrating successful operation over flights of more than 360 m trajectory and 70 m altitude change.

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Efficient integration of inertial observations into visual SLAM without initialization

Cited by 33 publications

References 4 publications

Mid-water current aided localization for autonomous underwater vehicles

Mid-water current aided localization for autonomous underwater vehicles

Deterministic initialization of metric state estimation filters for loosely-coupled monocular vision-inertial systems

Monocular Vision for Long‐term Micro Aerial Vehicle State Estimation: A Compendium

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