Egomotion estimation with optic flow and air velocity sensors

Rutkowski, A.; Miller, Mikel M.; Quinn, Roger D.; Willis, Mark A.

doi:10.1007/s00422-011-0440-z

Cited by 15 publications

(20 citation statements)

References 34 publications

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“…as the approximate inverse distance of the camera from the ground, with the assumption that ẑ and ẑ c are almost parallel (R 31 , R 32 R 33 ), equation (20) becomesα…”

Section: Estimation Of Inverse Distancementioning

confidence: 99%

“…Biomim. 13 (2018) 036004 [25] (also defined as apparent egovelocity in [20]). They provide the velocity information of the camera frame up to a scale factor (d).…”

Section: Optic Flow and Camera Motionmentioning

confidence: 99%

“…Recently, researchers have explored the integration of optic flow information with other known quantities [19] or measurements from other sensors to exploit the 'scaling' information, allowing true velocity or distance to be estimated [18,[20][21][22]. In [20], the opto-aeronautic algorithm was developed for estimation of wind speed and flight height using inputs from optic flow and air velocity sensors, whereas in [18], an ultrasonic sensor was incorporated.…”

Section: Introductionmentioning

confidence: 99%

“…In [20], the opto-aeronautic algorithm was developed for estimation of wind speed and flight height using inputs from optic flow and air velocity sensors, whereas in [18], an ultrasonic sensor was incorporated. In other instances, inertial measurement units (IMUs) are chosen as they often exist for attitude stabilization.…”

Section: Introductionmentioning

confidence: 99%

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A direct optic flow-based strategy for inverse flight altitude estimation with monocular vision and IMU measurements

Chirarattananon

2018

Bioinspir. Biomim.

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With tiny and limited nervous systems, insects demonstrate a remarkable ability to fly through complex environments. Optic flow has been identified to play a crucial role in regulating flight conditions and navigation in flies and bees. In robotics, optic flow has been widely studied thanks to the low computational requirements. However, with only monocular visual information, optic flow is inherently devoid of a scale factor required for estimating the absolute distance. In this paper, we propose a strategy for estimating the flight altitude of a flying robot with a ventral camera by combining the optic flow with measurements from an inertial measurement unit. Instead of using the prevalent feature-based approach for calculation of optic flow, we implement a direct method that evaluates the flow information via image gradients. We show that the direct approach notably simplifies the computation steps compared to the feature-based method. When combined with an extended Kalman filter for fusion of inertial measurement units measurements, the flight altitude can be estimated in real time. We carried out extensive flight tests in different settings. Among 31 hovering and vertical flights near the altitude of 40 cm, we achieved the RMS errors in the altitude estimate of 2.51 cm. Further analysis of factors that affect the quality of the flow and the distance estimate is also provided. PAPER Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.

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“…as the approximate inverse distance of the camera from the ground, with the assumption that ẑ and ẑ c are almost parallel (R 31 , R 32 R 33 ), equation (20) becomesα…”

Section: Estimation Of Inverse Distancementioning

confidence: 99%

“…Biomim. 13 (2018) 036004 [25] (also defined as apparent egovelocity in [20]). They provide the velocity information of the camera frame up to a scale factor (d).…”

Section: Optic Flow and Camera Motionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A direct optic flow-based strategy for inverse flight altitude estimation with monocular vision and IMU measurements

Chirarattananon

2018

Bioinspir. Biomim.

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“…In this context, also approaches to calibrate the scaling of an airspeed sensor have been developed [5,32].…”

Section: Related Workmentioning

confidence: 99%

Efficient probabilistic localization for autonomous indoor airships using sonar, air flow, and IMU sensors

Müller

Burgard

2013

Advanced Robotics

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In recent years, autonomous miniature airships have gained increased interest in the robotics community.Whereas their advantage lies in their abilities to move safely and to hover for extended periods of time, they at the same time are challenging as their payload is strictly limited and as their complex second-order kinematics makes the prediction of their pose and velocity through physical simulation difficult and imprecise. In this paper, we consider the problem of particle filter based online localization for a miniature blimp with lightweight ultrasound and air flow sensors as well as an IMU. We present probabilistic models dedicated to the special characteristics of the miniature and lightweight sensors applied on our blimp. Furthermore, we introduce an efficient odometry motion model based on the measurements of air flow sensors and an IMU which is less computationally demanding compared to the standard physical simulation-based control motion model. In experiments with a real blimp in a complex indoor environment, our approach has proven to allow accurate and reliable online localization of a miniature blimp and requires an order of magnitude fewer particles compared to the localization based on the standard control motion model. Furthermore, we demonstrate the substantial improvements in terms of localization accuracy when taking into account the temporal correlation of the air flow measurements in our novel odometry motion model.

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Monocular Snapshot-based Sensing and Control of Hover, Takeoff, and Landing for a Low-cost Quadrotor

Garratt

Lambert

2015

J. Field Robotics

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Autonomous control of a low‐cost micro air vehicle (MAV) is considered, using a single camera and inertial measurement unit. Integration of loom is used to determine the approximate height during takeoff and landing, and to provide an initial height for hover. Contrary to most optic‐flow‐based approaches that lack positional feedback, a “snapshot” paradigm is utilized in this paper that takes and stores an image of the ground under the MAV as a reference image, and the position of the vehicle can then be estimated from the transformation of the following images with respect to the reference image so that long‐term drift can be contained. In this new work, the implementation of the snapshot algorithm is considered with a focus on real‐time performance on a slow processor and the difficulties posed by illumination change and external disturbance. We also extend earlier work with extensive flight experiments conducted in various situations to evaluate the closed‐loop performance of the proposed algorithm.

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Egomotion estimation with optic flow and air velocity sensors

Cited by 15 publications

References 34 publications

A direct optic flow-based strategy for inverse flight altitude estimation with monocular vision and IMU measurements

A direct optic flow-based strategy for inverse flight altitude estimation with monocular vision and IMU measurements

Efficient probabilistic localization for autonomous indoor airships using sonar, air flow, and IMU sensors

Monocular Snapshot-based Sensing and Control of Hover, Takeoff, and Landing for a Low-cost Quadrotor

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