Mosaic based flexible navigation for AGVs

Lucas, Andre; Christo, C.; Silva, Miguel Pedro; Cardeira, Carlos

doi:10.1109/isie.2010.5637453

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…3 represents schematically the vision system geometry. As proposed by [10] the camera is located on top of the robot directed to the ceiling. It is aligned with the middle of the wheel axis line (where the IMU is also located).…”

Section: The Vision Systemmentioning

confidence: 99%

Tracking a Mobile Robot Position Using Vision and Inertial Sensor

Eleutério

Valtchev

Coito

2014

IFIP Advances in Information and Communication Technology

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Wheeled mobile robots are still the first choice when it comes to industrial or domotic applications. The robot's navigation system aims to reliably determine the robot's position, velocity and orientation and provide it to control and trajectory guidance modules. The most frequently used sensors are inertial measurement units (IMU) combined with an absolute position sensing mechanism. The dead reckoning approach using IMU suffers from integration drift due to noise and bias. To overcome this limitation we propose the use of the inertial system in combination with mechanical odometers and a vision based system. These two sensor complement each other as the vision sensor is accurate at low-velocities but requires long computation time, while the inertial sensor is able to track fast movements but suffers from drift. The information from the sensors is integrated through a multi-rate fusion scheme. Each of the sensor systems is assumed to have it's own independent sampling rate, which may be time-varying. Data fusion is performed by a multi-rate Kalman filter. The paper describes the inertial and vision navigation systems, and the data fusion algorithm. Simulation and experimental results are presented.

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Section: The Vision Systemmentioning

confidence: 99%

Tracking a Mobile Robot Position Using Vision and Inertial Sensor

Eleutério

Valtchev

Coito

2014

IFIP Advances in Information and Communication Technology

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“…As demonstrated by the huge amount of research work on the topic, image mosaicing is an established approach for autonomous or remotely mapping and real-time visualization. The technique has been indeed utilized as aid for robot path planning, navigation, and mapping on land (Kelly, 2000;Lucas et al, 2010;Wang et al, 2019) and underwater (Eustice, 2005;Gracias et al, 2003); for environmental monitoring through georeferenced video registration without a digital elevation model -DEM from unmanned aerial vehicles (Zhu et al, 2005); with video acquired with large format aerial vehicles (Molina and Zhu, 2014), for surveillance (Yang et al, 2015) and tracking of moving objects (Linger and Goshtasby, 2014); for constructing an overview of a target area with different sensors (RGB and/or thermal cameras) with and without metadata from the GPS and inertial navigation system (INS) from small-scale UAV (Yahyanejad, 2013); for supporting image interpretation and navigation in medical applications with microscopes (Loewke et al, 2010). In this work we describe a novel optimized approach of image mosaicing primarily tailored for the observation and real time mapping of the seabed.…”

Section: Introductionmentioning

confidence: 99%

3d Sequential Image Mosaicing for Underwater Navigation and Mapping

Nocerino

Menna

Chemisky

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Although fully autonomous mapping methods are becoming more and more common and reliable, still the human operator is regularly employed in many 3D surveying missions. In a number of underwater applications, divers or pilots of remotely operated vehicles (ROVs) are still considered irreplaceable, and tools for real-time visualization of the mapped scene are essential to support and maximize the navigation and surveying efforts. For underwater exploration, image mosaicing has proved to be a valid and effective approach to visualize large mapped areas, often employed in conjunction with autonomous underwater vehicles (AUVs) and ROVs. In this work, we propose the use of a modified image mosaicing algorithm that coupled with image-based real-time navigation and mapping algorithms provides two visual navigation aids. The first is a classic image mosaic, where the recorded and processed images are incrementally added, named 2D sequential image mosaicing (2DSIM). The second one geometrically transform the images so that they are projected as planar point clouds in the 3D space providing an incremental point cloud mosaicing, named 3D sequential image plane projection (3DSIP). In the paper, the implemented procedure is detailed, and experiments in different underwater scenarios presented and discussed. Technical considerations about computational efforts, frame rate capabilities and scalability to different and more compact architectures (i.e. embedded systems) is also provided.

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“…Image mosaics are among the most important targeted products of an aerial imaging system where the individual acquired images during the flight mission is stitched together to offer a large field of view of the surveyed scene. Image stitching/mosaicing has gained a lot of attention in many applications such as medical imagery (Can et al, 1999;Choe et al, 2006;Usmani et al, 2014), navigation (Lucas et al, 2010), and remote sensing (Turner et al, 2012;Wei et al, 2015). For constructing a stitched image, the transformations between the involved images have to be estimated based on overlapping areas between these images.…”

Section: Introductionmentioning

confidence: 99%

A Fast Approach for Stitching of Aerial Images

Moussa

El‐Sheimy

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image's coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.

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Mosaic based flexible navigation for AGVs

Cited by 5 publications

References 11 publications

Tracking a Mobile Robot Position Using Vision and Inertial Sensor

Tracking a Mobile Robot Position Using Vision and Inertial Sensor

3d Sequential Image Mosaicing for Underwater Navigation and Mapping

A Fast Approach for Stitching of Aerial Images

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