Visual odometry and map fusion for GPS navigation assistance

Parra, I.; Sotelo, Miguel Ángel; Llorca, David Fernández; Fernández, Carlos; Llamazares, Ángel; Hernández, Noelia; García, I.

doi:10.1109/isie.2011.5984266

Cited by 22 publications

(13 citation statements)

References 12 publications

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“…Another interesting improvement is the fusion of the visual ego motion estimation and the raw data of the GPS [21] in order to cope with GPS raw data loss and reduce cumulative error of the visual ego motion.…”

Section: Discussionmentioning

confidence: 99%

Visual ego motion estimation in urban environments based on U-V disparity

Musleh

Martín

Escalera

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Visual Ego Motion Estimation in Urban Environments based on U-V DisparityBasam Musleh, David Martin, Arturo de la Escalera and Jose Maria ArmingolAbstract-The movement of the vehicle provides useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by means of a GPS, but there are some areas in urban environments where the signal is not available, as tunnels or streets with high buildings. A new method for 2D visual ego motion estimation in urban environments is presented in this paper. This method is based on a stereo-vision system where the feature road points are tracked frame to frame in order to estimate the movement of the vehicle, avoiding outliers from dynamic obstacles. The road profile is used to obtain the world coordinates of the feature points as a unique function of its left image coordinates. For these reasons it is only necessary to search feature points in the lower third of the left images. Moreover, the Kalman filter is used as a solution for filtering problem. That is, in some cases, it is necessary to filter raw data due to noise acquisition of time series. The results of the visual ego motion are compared with raw data from a GPS.

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Section: Discussionmentioning

confidence: 99%

Visual ego motion estimation in urban environments based on U-V disparity

Musleh

Martín

Escalera

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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“…Local constraints are intrinsically affected by a small cumulative drift: to overcome this problem, we integrate in the graph drift-free global measurements as position prior information. In particular, we define a GPS prior z GP S The IMU is used as a drift-free roll and pitch reference 6 , where the drift resulting from the gyroscopes integration is compensated by using the accelerometers data.…”

Section: )mentioning

confidence: 99%

“…Since we assume that the altitude varies slowly, we can use the current position estimate T i (i.e., the t x,i and t y,i components) to query the DEM for a reliable altitude estimation z DEM,i = f (t x,i , t y,i ), with associated information matrix Ω DEM i . The cost function is then assembled as follows: 6 We experienced that integrating the full inertial information inside the optimization did not positively affect the state estimation: our intuition is that the slow, often unimodal, motion of our robot makes the IMU biases difficult to estimate and sometimes predominant over the motion components.…”

Section: )mentioning

confidence: 99%

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An Effective Multi-Cue Positioning System for Agricultural Robotics

Imperoli

Potena

Nardi

et al. 2018

IEEE Robot. Autom. Lett.

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The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, . . . ), and the noise introduced by raw GPS readings. Along with a suitable motion model, our system also integrates two additional types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random Field assumption. We demonstrate how using these additional cues substantially reduces the error along the altitude axis and, moreover, how this benefit spreads to the other components of the state. We report exhaustive experiments combining several sensor setups, showing accuracy improvements ranging from 37% to 76% with respect to the exclusive use of a GPS sensor. We show that our approach provides accurate results even if the GPS unexpectedly changes positioning mode. The code of our system along with the acquired datasets are released with this paper.Index Terms-Robotics in Agriculture and Forestry, Localization and Sensor Fusion SUPPLEMENTARY MATERIALThe datasets and the project's code are available at:

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“…Najjar and Bonnifait [39] further integrated Kalman filter based fusion method with a belief theory. Parra et al [41] proposed to improve GPS readings with visual odometry only when the GPS system is not reliable (e.g., Horizontal Dilution Of Position is greater than 10). The above data fusion methods considered camera locations as a set of disjoint points and tried to refine them separately, ignoring the fact that camera trajectories are defined in a continuous time-geography space.…”

Section: Relationships To Previous Workmentioning

confidence: 99%

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2018

TIST

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This article presents a high-precision multi-modal approach for localizing moving cameras with monocular videos, which has wide potentials in many intelligent applications, including robotics, autonomous vehicles, and so on. Existing visual odometry methods often suffer from symmetric or repetitive scene patterns, e.g., windows on buildings or parking stalls. To address this issue, we introduce a robust camera localization method that contributes in two aspects. First, we formulate feature tracking, the critical step of visual odometry, as a hierarchical min-cost network flow optimization task, and we regularize the formula with flow constraints, cross-scale consistencies, and motion heuristics. The proposed regularized formula is capable of adaptively selecting distinctive features or feature combinations, which is more effective than traditional methods that detect and group repetitive patterns in a separate step. Second, we develop a joint formula for integrating dense visual odometry and sparse GPS readings in a common reference coordinate. The fusion process is guided with high-order statistics knowledge to suppress the impacts of noises, clusters, and model drifting. We evaluate the proposed camera localization method on both public video datasets and a newly created dataset that includes scenes full of repetitive patterns. Results with comparisons show that our method can achieve comparable performance to state-of-the-art methods and is particularly effective for addressing repetitive pattern issues.

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Visual odometry and map fusion for GPS navigation assistance

Cited by 22 publications

References 12 publications

Visual ego motion estimation in urban environments based on U-V disparity

Visual ego motion estimation in urban environments based on U-V disparity

An Effective Multi-Cue Positioning System for Agricultural Robotics

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