An experimental study on using visual odometry for short-run self localization of field robot

Piyathilaka, Lasitha; Munasinghe, Rohan

doi:10.1109/iciafs.2010.5715651

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…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

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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.

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Section: Upper and Lower Boundmentioning

confidence: 99%

Globally-Optimal Event Camera Motion Estimation

Peng,

Wang,

Gao

et al. 2022

Preprint

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“…A forward-facing setup provides more information, but it is a suboptimal solution for detecting small movements. Moreover, it can be obscured by shadows and surround changes, such as wind and sunlight [124]. On the other hand, localization systems based on downward-facing cameras have been successfully used for positioning in pre-explored environments.…”

Section: Camera Posementioning

confidence: 99%

A Survey on Odometry for Autonomous Navigation Systems

et al. 2019

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The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, selfcontained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.

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“…A multi-template correlation matching is presented in [26] where different quality responses as median, absolute deviations, entropy, cross-correlation, variance, are evaluated for selecting the best template candidate. The authors of [27] have proposed the fast Fourier transform (FFT)-based image registration method for determining accurate translational information of ground robot. The phase correlation of two input images is analyzed in order to find the impulse corresponding to the location of maximum translation.…”

Section: Related Workmentioning

confidence: 99%

A Vision-Based Odometer for Localization of Omnidirectional Indoor Robots

Patruno

Colella

Nitti

et al. 2020

Sensors

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In this paper we tackle the problem of indoor robot localization by using a vision-based approach. Specifically, we propose a visual odometer able to give back the relative pose of an omnidirectional automatic guided vehicle (AGV) that moves inside an indoor industrial environment. A monocular downward-looking camera having the optical axis nearly perpendicular to the ground floor, is used for collecting floor images. After a preliminary analysis of images aimed at detecting robust point features (keypoints) takes place, specific descriptors associated to the keypoints enable to match the detected points to their consecutive frames. A robust correspondence feature filter based on statistical and geometrical information is devised for rejecting those incorrect matchings, thus delivering better pose estimations. A camera pose compensation is further introduced for ensuring better positioning accuracy. The effectiveness of proposed methodology has been proven through several experiments, in laboratory as well as in an industrial setting. Both quantitative and qualitative evaluations have been made. Outcomes have shown that the method provides a final positioning percentage error of 0.21% on an average distance of 17.2 m. A longer run in an industrial context has provided comparable results (a percentage error of 0.94% after about 80 m). The average relative positioning error is about 3%, which is still in good agreement with current state of the art.

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An experimental study on using visual odometry for short-run self localization of field robot

Cited by 12 publications

References 6 publications

Globally-Optimal Event Camera Motion Estimation

Globally-Optimal Event Camera Motion Estimation

A Survey on Odometry for Autonomous Navigation Systems

A Vision-Based Odometer for Localization of Omnidirectional Indoor Robots

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