Ground plane based visual odometry for RGBD-Cameras using orthogonal projection

Jordan, Julian; Zell, Andreas

doi:10.1016/j.ifacol.2016.07.717

Cited by 6 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many existing methods therefore do not employ feature correspondences but aim at a correspondence-less alignment or even a full photometric image alignment. Besides more classical RANSAC-based hypothesise-and-test schemes [7], the community therefore has also developed appearance-based template matching approaches [8,23,33,22,15], solvers based on efficient second-order minimisation [20,38,18], and methods exploiting the Fast Fourier Transform [25,2], the Fourier-Mellin Transform [16,19], or the Improved Fourier Mellin Invariant [31,4]. In an attempt to tackle highly self-similar ground textures, Dille et al [8] propose to use an optical flow sensor instead of a regular CMOS camera.…”

Section: Upper and Lower Boundmentioning

confidence: 99%

Globally-Optimal Event Camera Motion Estimation

Peng,

Wang,

Gao

et al. 2022

Preprint

View full text Add to dashboard Cite

Event cameras are bio-inspired sensors that perform well in HDR conditions and have high temporal resolution. However, different from traditional frame-based cameras, event cameras measure asynchronous pixel-level brightness changes and return them in a highly discretised format, hence new algorithms are needed. The present paper looks at fronto-parallel motion estimation of an event camera. The flow of the events is modeled by a general homographic warping in a space-time volume, and the objective is formulated as a maximisation of contrast within the image of unwarped events. However, in stark contrast to prior art, we derive a globally optimal solution to this generally non-convex problem, and thus remove the dependency on a good initial guess. Our algorithm relies on branch-and-bound optimisation for which we derive novel, recursive upper and lower bounds for six different contrast estimation functions. The practical validity of our approach is supported by a highly successful application to AGV motion estimation with a downward facing event camera, a challenging scenario in which the sensor experiences fronto-parallel motion in front of noisy, fast moving textures.

show abstract

Section: Upper and Lower Boundmentioning

confidence: 99%

Globally-Optimal Event Camera Motion Estimation

Peng,

Wang,

Gao

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The matching is often based on a distance between the vectors, e.g. the Mahanalobis or Euclidean distance (Bian et al, 2017;Jordan & Zell, 2016). Finally, mismatch elimination should be dealt with to obtained origin match by implementing above steps.…”

Section: Introductionmentioning

confidence: 99%

An improved GMS-PROSAC algorithm for image mismatch elimination

Zhao

Ding

Wang

et al. 2018

Systems Science & Control Engineering

View full text Add to dashboard Cite

Image matching usually plays a critical role for visual simultaneous localization and mapping (VSLAM). However, the resultant mismatches and low calculation efficiency of current matching algorithms reduces the performance of VSLAM. In order to overcome above two drawbacks, an improved GMS-PROSAC algorithm for image mismatch elimination is developed in this paper by introducing a new epipolar geometric constraint (EGC) model with a projection error function. This improved algorithm, named as GMS-EGCPROSAC, is made up of the traditional GMS algorithm and the improved PROSAC algorithm. First, the GMS algorithm is employed to obtain a rough matching set and then all matching pairs in this set are sorted according to their similarity degree. By selecting some smatching pairs with the highest similarity degree, the parameter of the EGC model is obtained. By resort to the calculated parameter, the improved PROSAC algorithm can be carried out to eliminate false matches. Finally, the real-time and the effectiveness are adequately verified by executing some contrast experiments. Our approach can not only quickly eliminate mismatches but also get more high-quality matching pairs.

show abstract