An Inertially Stabilised Vehicle Camera System - Hardware, Algorithms, Test Drives

Günthner, W. A.; Wagner, Philipp; Ulbrich, H.

doi:10.1109/iecon.2006.347231

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Its two wide angle cameras, shown in [4], are also used for stereo-based depth estimation. The tele camera, shown in [5], is used for early recognition of obstacles. Two lidar sensors provide range data for obstacles and improve object detection in a data fusion stage as shown in Sec.…”

Section: Introductionmentioning

confidence: 99%

Design and capabilities of the Munich Cognitive Automobile

Goebl

Althoff

Buss

et al. 2008

2008 IEEE Intelligent Vehicles Symposium

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This paper presents the design of the Cognitive Automobile in Munich. The focus of the capabilities shown here is the navigation on highways and rural roads. The emphasis on higher speed requires early detection of far field objects, so a multi focal active vision with gaze control is essential. For increased robustness lidar range sensors are combined with vision using an object fusion approach. An elaborate safety concept and a verification stage ensure a safe behavior of the vehicle in all situations. A communication system enables the vehicle to perform cooperative perception and action together with similar intelligent vehicles. platform Active camera Vehicle actuators and sensors

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Section: Introductionmentioning

confidence: 99%

Design and capabilities of the Munich Cognitive Automobile

Goebl

Althoff

Buss

et al. 2008

2008 IEEE Intelligent Vehicles Symposium

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“…With the exception of [10], the above methods are not suited to compensating for large motions encountered, for example, in walking. Consequently, humanoid robots often rely on the larger range of whole camera motions to perform gaze stabilization [3,24,21,8,14].…”

Section: Related Workmentioning

confidence: 99%

“…by moving the internal camera optics in response to the sensed inertial motion [4,20]. In [10], an onboard camera is stabilized against vehicle vibration by panning and tilting either a set of external mirrors or the camera itself. Alternatively, one can capture the image as is (without stabilization) and use digital postprocessing to correct the motion blurred image [27,12].…”

Section: Related Workmentioning

confidence: 99%

Active gaze stabilization

Lesmana¹,

Landgren

Forssén

et al. 2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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We describe a system for active stabilization of cameras mounted on highly dynamic robots. To focus on careful performance evaluation of the stabilization algorithm, we use a camera mounted on a robotic test platform that can have unknown perturbations in the horizontal plane, a commonly occurring scenario in mobile robotics. We show that the camera can be effectively stabilized using an inertial sensor and a single additional motor, without a joint position sensor. The algorithm uses an adaptive controller based on a model of the vertebrate Cerebellum for velocity stabilization, with additional drift correction. We have also developed a resolution adaptive retinal slip algorithm that is robust to motion blur.We evaluated the performance quantitatively using another high speed robot to generate repeatable sequences of large and fast movements that a gaze stabilization system can attempt to counteract. Thanks to the high-accuracy repeatability, we can make a fair comparison of algorithms for gaze stabilization. We show that the resulting system can reduce camera image motion to about one pixel per frame on average even when the platform is rotated at 200 degrees per second. As a practical application, we also demonstrate how the common task of face detection benefits from active gaze stabilization.

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