Robust embedded egomotion estimation

Voigt,; Nikolić,; Hurzeler,; Weiss,; Kneip, Laurent; Siegwart, Roland

doi:10.1109/iros.2011.6048855

Cited by 17 publications

(19 citation statements)

References 11 publications

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“…The rover's wheel encoders and an inertial navigation system are fused in an extended information filter. An EKF presented by Voigt et al [34] fuses binocular visual measurements and inertial cues for egomotion estimation of an aerial vehicle. Their approach relies more on inertial data if visual feature constellation is degenerate and enables pose estimation at frame rate on their platform.…”

Section: Egomotionmentioning

confidence: 99%

Probabilistic terrain classification in unstructured environments

Häselich

Arends

Wojke

et al. 2013

Robotics and Autonomous Systems

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Section: Egomotionmentioning

confidence: 99%

Probabilistic terrain classification in unstructured environments

Häselich

Arends

Wojke

et al. 2013

Robotics and Autonomous Systems

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“…This may be done by using, e.g., SURF ( [16], [8]), FAST ( [20], [5], [8]) or Harris corners ( [13], [5], [10]). These features then serve as measurements for determining the translation and rotation from consecutive image frames by applying the epipolar constraint, using either the eightpoint algorithm [4], five-point algorithm [12], or one of their variants.…”

Section: A Related Workmentioning

confidence: 99%

“…These features then serve as measurements for determining the translation and rotation from consecutive image frames by applying the epipolar constraint, using either the eightpoint algorithm [4], five-point algorithm [12], or one of their variants. Good results can be achieved through combination with techniques like Kalman filtering [3] or robust estimation methods like RANSAC ( [10], [13], [20], [3]). …”

Section: A Related Workmentioning

confidence: 99%

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Mono-camera based pitch rate estimation in nighttime scenarios

Schule

Schweiger

Hartmann

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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The camera relative pose is essential information for driver assistance systems in general, and especially so for systems that aim to visualize obstacles or other relevant objects for the driver. In order to display objects that were not detected in the vision frame of the camera (e.g. road information of a digital map), the exact transformation from camera to the world has to be known for every time frame. The horizontal movement of the camera in the real world is well known due to sufficiently accurate inertial sensors and wheel speed sensors available in the car. In contrast, the vertical movement is both unknown and highly dynamic. Relative pose estimation is a widely used technique that determines a vehicle's movement and rotation from frame to frame. These techniques rely primarily on point correspondences computed beforehand. In the nighttime case, these methods fail because of the poor image quality and low number of correspondences. This paper presents a method that fuses vehicle movement information with the features extracted from a mono camera. By restricting the problem to pitch estimation only, this method allows robust and accurate estimation of the vehicle's pitch movements even in nighttime scenarios. A highly accurate inertial navigation system is used to evaluate the results obtained in challenging real world nighttime video sequences. Experiments show the feasibility and robustness of the proposed approach.

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“…Voigt, et al [6] present a global estimation method that also uses an EKF to combine stereo visual odometry and IMU information. To make the relative visual odometry update global, additional variables that account for the position and orientation (pose) of the last image are added to the state.…”

Section: Introductionmentioning

confidence: 99%

Relative navigation and control of a hexacopter

Leishman

Macdonald

McLain

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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This paper discusses the progress made on developing a multi-rotor helicopter equipped with a vision-based ability to navigate through an a priori unknown, GPS-denied environment. We highlight the backbone of our system, the relative estimation and control. We depart from the common practice of using a globally referenced map, preferring instead to keep the position and yaw states in the EKF relative to the current map node. This relative navigation approach allows simple application of sensor updates, natural characterization of the transformation between map nodes, and the potential to generate a globally consistent map when desired. The EKF fuses view matching data from a Microsoft Kinect with more frequent IMU data to provide state estimates at rates high enough to control the vehicles fast dynamics. Although an EKF is used, a nodes and edges graph represents the map. Hardware results showing the quality of the estimates and flights with estimates in the loop are provided.

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Robust embedded egomotion estimation

Cited by 17 publications

References 11 publications

Probabilistic terrain classification in unstructured environments

Probabilistic terrain classification in unstructured environments

Mono-camera based pitch rate estimation in nighttime scenarios

Relative navigation and control of a hexacopter

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