Stereo-based visual localization without triangulation for unmanned robotics platform

Volkov, A. N.; Ershov, Egor; Gladilin, Sergey; Nikolaev, Dmitry P.

doi:10.1117/12.2266410

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Meanwhile comparison of the filtering accuracy shows almost identical accuracy [ 49 ], that is why one should prefer most simple pseudomeasurement Kalman filter without bias, developed on the basis of Pugachev’s conditionally optimal filtering [ 50 , 51 , 52 , 53 ]. To obtain 3D coordinates, it is also possible to measure the range, which is possible using stereo systems [ 54 , 55 , 56 ] or using active radio or laser range finders [ 6 ]. The latter can be limited in use because they need essential power and disclose the UAV position, and the stereo systems require very accurate calibration and need the creation of a significant triangulation base which is rather difficult to maintain on small-sized UAV in flight.…”

Section: General Approach To the Data Fusion Model Of Uav Navigatimentioning

confidence: 99%

New Approaches to the Integration of Navigation Systems for Autonomous Unmanned Vehicles (UAV)

Konovalenko

Kuznetsova

Miller

et al. 2018

Sensors

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The article presents an overview of the theoretical and experimental work related to unmanned aerial vehicles (UAVs) motion parameters estimation based on the integration of video measurements obtained by the on-board optoelectronic camera and data from the UAV’s own inertial navigation system (INS). The use of various approaches described in the literature which show good characteristics in computer simulations or in fairly simple conditions close to laboratory ones demonstrates the sufficient complexity of the problems associated with adaption of camera parameters to the changing conditions of a real flight. In our experiments, we used computer simulation methods applying them to the real images and processing methods of videos obtained during real flights. For example, it was noted that the use of images that are very different in scale and in the aspect angle from the observed images in flight makes it very difficult to use the methodology of singular points. At the same time, the matching of the observed and reference images using rectilinear segments, such as images of road sections and the walls of the buildings look quite promising. In addition, in our experiments we used the projective transformation matrix computation from frame to frame, which together with the filtering estimates for the coordinate and angular velocities provides additional possibilities for estimating the UAV position. Data on the UAV position determining based on the methods of video navigation obtained during real flights are presented. New approaches to video navigation obtained using the methods of conjugation rectilinear segments, characteristic curvilinear elements and segmentation of textured and colored regions are demonstrated. Also the application of the method of calculating projective transformations from frame-to-frame is shown which gives estimates of the displacements and rotations of the apparatus and thereby serves to the UAV position estimation by filtering. Thus, the aim of the work was to analyze various approaches to UAV navigation using video data as an additional source of information about the position and velocity of the vehicle.

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Section: General Approach To the Data Fusion Model Of Uav Navigatimentioning

confidence: 99%

New Approaches to the Integration of Navigation Systems for Autonomous Unmanned Vehicles (UAV)

Konovalenko

Kuznetsova

Miller

et al. 2018

Sensors

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“…The majority of known autonomous ground vehicles use a predefined map as a reference and derive their global pose by associating incoming sensory data with mapped data corresponding to a particular reference pose (Chausse et al 2005;Tao et al 2013;Volkov et al 2017). As shown in (Li et al 2017), size is an important property of a digital map.…”

Section: Introductionmentioning

confidence: 99%

Ground Vehicle Localization With Particle Filter Based On Simulated Road Marking Image

Shipitko¹,

Grigoryev²

2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

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Precise localization is a prerequisite and a cornerstone for successful operation of any autonomous vehicle. In this paper, consideration is given to a lane feature-based approach to a self-driving vehicle localization. Proposed map-relative localization method is built upon a combination of vision-based lane markings detection and odometry data. Detected lane markings are aligned with a reference map in order to derive global pose estimate while odometry provides path consistency. To combine heterogeneous sensory data use is made of particle filter method. It allows for non-Gaussian noise common for vision-based detectors as well as a further extension of data sources. The approach described in this work was tested on a real vehicle in urban environment and proved itself to be precise and reliable enough for real-world applications. It was able to provide lateral and longitudinal map-relative localization with a precision of 0.2 m.

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Unmanned Terminal Vehicle Positioning System Based on Roadside Single-Line Lidar

Chen

Huang

2021

SAE Technical Paper Series

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Stereo-based visual localization without triangulation for unmanned robotics platform

Cited by 3 publications

References 5 publications

New Approaches to the Integration of Navigation Systems for Autonomous Unmanned Vehicles (UAV)

New Approaches to the Integration of Navigation Systems for Autonomous Unmanned Vehicles (UAV)

Ground Vehicle Localization With Particle Filter Based On Simulated Road Marking Image

Unmanned Terminal Vehicle Positioning System Based on Roadside Single-Line Lidar

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