Where did I take that snapshot? Scene-based homing by image matching

Franz, Matthias; Schölkopf, Bernhard; Mallot, Hanspeter A.; Bülthoff, HH

doi:10.1007/s004220050470

Cited by 179 publications

(177 citation statements)

References 31 publications

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“…If no depth information is available, though, only the directions of the flow vectors in the two matched filters are known, but not their length. Our prediction therefore rests on an "equal-distance assumption" as the warping method (Franz et al 1998b). The performance of the MFDID method shows that, as in the warping method, even severe violations of this assumption have only mild effects on the home vector direction.…”

Section: Homing With Two Flow Templatesmentioning

confidence: 93%

“…Since we have no information about object distances, we continue with an equal-distance assumption (see, e.g., Franz et al 1998b): D is assumed to be constant for all directions ϕ. We finally get an equation for the home vector in Eq.…”

Section: Descent In Distance Measuresmentioning

confidence: 99%

“…Both our approach and the warping method introduced by Franz et al (1998b) use matched filters for local visual homing. In what follows, we highlight the differences between the two methods.…”

Section: Mfdid Versus Warpingmentioning

confidence: 99%

“…For the warping method it could be proved mathematically that the error due to this assumption decreases when the agent approaches the goal (Franz et al 1998b); whether this property also holds for MFDID is still an open question. In the practical application, violations of the equal distance assumption appear to have only mild effects on the overall performance of either warping and MFDID.…”

Section: Mfdid Versus Warpingmentioning

confidence: 99%

“…The warping method proposed by Franz et al (1998b) is essentially a process of "mentally" simulating how different movements would distort (warp) the current image. By searching through the space of movement parameters for that distorted image which best fits the stored snapshot, the method produces estimates for the home direction, the orientation of the agent, and the distance from home.…”

Section: Introductionmentioning

confidence: 99%

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Local visual homing by matched-filter descent in image distances

Möller

Vardy

2006

Biol Cybern

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In natural images, the distance measure between two images taken at different locations rises smoothly with increasing distance between the locations. This fact can be exploited for local visual homing where the task is to reach a goal location that is characterized by a snapshot image: descending in the image distance will lead the agent to the goal location. To compute an estimate of the spatial gradient in the distance measure, its value must be sampled at three noncollinear points. An animal or robot would have to insert exploratory movements into its home trajectory to collect these samples. Here we suggest a method based on the matched-filter concept that allows one to estimate the gradient without exploratory movements. Two matched filters -optical flow fields resulting from translatory movements in the horizontal plane -are used to predict two images in perpendicular directions from the current location. We investigate the relation to differential flow methods applied to the local homing problem and show that the matched-filter approach produces reliable homing behavior on image databases. Two alternative methods that only require a single matched filter are suggested. The matched-filter concept is also applied to derive a home-vector equation for a Fourier-based parameter method.

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Section: Homing With Two Flow Templatesmentioning

confidence: 93%

Section: Descent In Distance Measuresmentioning

confidence: 99%

Section: Mfdid Versus Warpingmentioning

confidence: 99%

Section: Mfdid Versus Warpingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local visual homing by matched-filter descent in image distances

Möller

Vardy

2006

Biol Cybern

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Comparing parallel hardware architectures for visually guided robot navigation

Schenck

Horst

Tiedemann

et al. 2016

Concurrency and Computation

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Summary Local visual homing methods are a family of algorithms for visually guided navigation on mobile robots. Within this family, the so‐called min‐warping algorithm yields very precise results but is rather compute‐intensive. For this reason, we developed several implementations of this algorithm for different parallel hardware architectures (multi‐core CPUs with SIMD extensions, graphics processing units (GPUs), field‐programmable gate array) to arrive at a fast and energy‐efficient solution which is suited for real‐time performance on mobile platforms with limited battery capacity. Because the min‐warping algorithm is also well suited as a general benchmark, we carried out a comprehensive comparison study which includes both speed and real‐power measurements and covers both low‐power processors and high‐end devices. Our findings suggest that field‐programmable gate arrays offer the most energy‐efficient platform for min‐warping in the area of low‐power processors, while GPUs take the lead in the area of high‐end devices. However, as soon as the full capabilities of modern CPUs (like vector execution units and multiple hardware threads) are used, the speedup advantage of GPUs goes down to the single digit range. Copyright © 2016 John Wiley & Sons, Ltd.

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The visual compass: Performance and limitations of an appearance‐based method

Labrosse

2006

Journal of Field Robotics

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In this article we present an algorithm to estimate the orientation of a robot relative to an orientation specified at the beginning of the process. This is done by computing the rotation of the robot between successive panoramic images, grabbed on the robot while it moves, using a subsymbolic method to match the images. The context of the work is simultaneous localization and mapping ͑SLAM͒ in unstructured and unmodified environments. As such, very few assumptions are made about the environment and the robot's displacement. The algorithm's performance depends on the value of a number of parameters being determined to provide overall good performance of the system. The performance is evaluated in different situations ͑trajectories and environments͒ with the same parameters and the results show that the method performs adequately for its intended use. In particular, the error is shown to be drifting slowly, in fact much slower than unprocessed inertial sensors, thus only requiring infrequent realignment, for example, when relocalizing in a topological map. Limitations of the proposed methods are also shown and discussed. Figure 23. CARPARK: error between the magnetic and visual orientations.

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Where did I take that snapshot? Scene-based homing by image matching

Cited by 179 publications

References 31 publications

Local visual homing by matched-filter descent in image distances

Local visual homing by matched-filter descent in image distances

Comparing parallel hardware architectures for visually guided robot navigation

The visual compass: Performance and limitations of an appearance‐based method

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