Full-3D Edge Tracking with a Particle Filter

Klein, Georg; Murray, David

doi:10.5244/c.20.114

Cited by 76 publications

(91 citation statements)

References 14 publications

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“…The first instantiation of our system computes the observation function with image edge weighted equally , rather than weighting each pixel equally as in Klein and Murray (2006). Computing the observation function on a local basis gives equal weighting to each edge, avoiding the observation function being dominated by large edges, so that smaller edges are not ignored in situations in which they are in fact providing useful localization information.…”

Section: Related Workmentioning

confidence: 99%

“…The map is projected onto the image plane so that a direct comparison can be made. A fast method is presented in Klein and Murray (2006) that performs this computation on a GPU counting the number of aligning pixels over the whole image. This section will first describe Klein and Murray's metric and then describe modifications that improve performance.…”

Section: Observation Functionmentioning

confidence: 99%

“…Recently in Klein and Murray (2006), a particle filter method was presented that maintains many pose estimates per frame. The technique was applied to the tracking of a single object, such as a printer, from a range of just a few meters in a regular indoor environment.…”

Section: Particle Filter Localizationmentioning

confidence: 99%

“…A 3D-edge particle filter was presented in Klein and Murray (2006), which uses an observation function for each particle that measures the quality of alignment of the 3D-edge map with the camera image. This system is tested and evaluated against our proposed system.…”

Section: Related Workmentioning

confidence: 99%

“…We have developed an edge-based localization system inspired by the work in Klein and Murray (2006). This algorithm uses a particle filter that maintains multiple pose hypotheses in which each particle represents a possible pose of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Robust outdoor visual localization using a three‐dimensional‐edge map

Nuske

Roberts

Wyeth

2009

Journal of Field Robotics

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Visual localization systems that are practical for autonomous vehicles in outdoor industrial applications must perform reliably in a wide range of conditions. Changing outdoor conditions cause difficulty by drastically altering the information available in the camera images. To confront the problem, we have developed a visual localization system that uses a surveyed three-dimensional (3D)-edge map of permanent structures in the environment. The map has the invariant properties necessary to achieve long-term robust operation. Previous 3D-edge map localization systems usually maintain a single pose hypothesis, * Currently Postdoctoral Fellow at the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 (e-mail: nuske@cmu.edu). making it difficult to initialize without an accurate prior pose estimate and also making them susceptible to misalignment with unmapped edges detected in the camera image. A multihypothesis particle filter is employed here to perform the initialization procedure with significant uncertainty in the vehicle's initial pose. A novel observation function for the particle filter is developed and evaluated against two existing functions. The new function is shown to further improve the abilities of the particle filter to converge given a very coarse estimate of the vehicle's initial pose. An intelligent exposure control algorithm is also developed that improves the quality of the pertinent information in the image. Results gathered over an entire sunny day and also during rainy weather illustrate that the localization system can operate in a wide range of outdoor conditions. The conclusion is that an invariant map, a robust multihypothesis localization algorithm, and an intelligent exposure control algorithm all combine to enable reliable visual localization through challenging outdoor conditions. C 2009 Wiley Periodicals, Inc. Journal of Field

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Section: Related Workmentioning

confidence: 99%

Section: Observation Functionmentioning

confidence: 99%

Section: Particle Filter Localizationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust outdoor visual localization using a three‐dimensional‐edge map

Nuske

Roberts

Wyeth

2009

Journal of Field Robotics

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Premebida

Ludwig

Nunes

2009

Journal of Field Robotics

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Visual-Inertial Object Detection and Mapping

Fei

Soatto

2018

Computer Vision – ECCV 2018

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We present a method to populate an unknown environment with models of previously seen objects, placed in a Euclidean reference frame that is inferred causally and on-line using monocular video along with inertial sensors. The system we implement returns a sparse point cloud for the regions of the scene that are visible but not recognized as a previously seen object, and a detailed object model and its pose in the Euclidean frame otherwise. The system includes bottom-up and topdown components, whereby deep networks trained for detection provide likelihood scores for object hypotheses provided by a nonlinear filter, whose state serves as memory. Additional networks provide likelihood scores for edges, which complements detection networks trained to be invariant to small deformations. We test our algorithm on existing datasets, and also introduce the VISMA dataset, that provides ground truth pose, point-cloud map, and object models, along with time-stamped inertial measurements.

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Full-3D Edge Tracking with a Particle Filter

Cited by 76 publications

References 14 publications

Robust outdoor visual localization using a three‐dimensional‐edge map

Robust outdoor visual localization using a three‐dimensional‐edge map

Untitled

Visual-Inertial Object Detection and Mapping

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