Autonomous Vehicle Navigation Using Vision and Mapless Strategies: A Survey

Güzel, Mehmet Serdar

doi:10.1155/2013/234747

Cited by 26 publications

(16 citation statements)

References 46 publications

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“…The software constitutes an MTX-lynx core, a module containing a robot controller and power management, and an IPC with micro Windows, which are all pre-installed. The map-building-based approach entails exploring the environment and building a representation map of the environment, 29 and the robot perceives and detects the landmarks for SLAM mainly depending on the camera's successive visual scenes. In our method, the LRF combined with the visual system embedded An outdoor experimental set-up is shown in Figure 6; a crawler-I LUKER is installed with URG-04LX which is a laser scanning distance sensor.…”

Section: Processmentioning

confidence: 99%

A perception-driven exploration hierarchical simultaneous localization and mapping for mobile robots adapted to search and rescue environments

Wang

Zhang

Song

et al. 2018

Advances in Mechanical Engineering

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Perception-driven hierarchical simultaneous localization and mapping approach is proposed based on a distributed particle of percolator model adapted to search and rescue post-disaster environment. The proposed hierarchical analysis reduced the complexity; it can only highlight the interesting three-dimensional objects marked on the two-dimensional map, which adapted to urgent search and rescue post-disaster scenarios. Object identification, area coverage, and loop closure are the key issues that simultaneous localization and mapping faces when mobile robots detect and reach survivors in search and rescue collapsed environments. We developed hierarchical analysis using iterative extended Kalman filter method to improve the localization accuracy in simultaneous localization and mapping of mobile robots with an iteration process. An analytic hierarchical map-building mechanism efficiently constructs a globally consistent topological map using graph-based nodes and data association. An improved iterative extended Kalman filter procedure is used to estimate positions and the covariance matrix. The proposed algorithm incorporates a prediction step and a measurement update step, which performs the state vector time update in constant time interval and the correction of the state vector based on the new sensor measurement. The hierarchical mapping process is divided into three levels. The bottom level perceives the surroundings and collects input. The middle level receives and processes data from the bottom level. The upper level organizes the feature information from the previous levels to form a global, topologically consistent map. Simulations and experiments using the mobileRobot platform, Pioneer LX robot, and a crawler mobile robot validate the efficiency of this hierarchical simultaneous localization and mapping approach adapting to search and rescue post-disaster scenarios.

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Section: Processmentioning

confidence: 99%

A perception-driven exploration hierarchical simultaneous localization and mapping for mobile robots adapted to search and rescue environments

Wang

Zhang

Song

et al. 2018

Advances in Mechanical Engineering

View full text Add to dashboard Cite

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“…These tasks are already accomplished by vision-based robots, which are nowadays built with inexpensive equipment and have an easy maintenance. Several techniques to endow vehicles with vision-based control can be found in (Güzel, 2013;Bertozzi & Broggi, 1997;Tisdale, et al, 2009;Carloni, et al, 2013;Yunji, et al, 2013;Ivancsits & Lee, 2013;Lim, et al, 2012;Mahony, et al, 2012;Guillet, et al, 2014;Mcgill & Taylor, 2011). They enable object detection, path orientation and distance control.…”

Section: Introductionmentioning

confidence: 99%

“…Another example of mobile robot, now with a video camera for navigation aid, was developed in the 80s at Carnegie Mellon University (Güzel, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…These different approaches make unmanned vehicles over uncovered areas render different mappings. See Güzel, 2013;Bertozzi & Broggi, 1997;Tisdale, et al, 2009;Carloni, et al, 2013;Yunji, et al, 2013;Ivancsits & Lee, 2013;Lim, et al, 2012;Mahony, et al, 2012) for an overview. Some related to projects aim at a geographical image mapping that gives relevant data to agricultural activity (Guillet, 2014;Medeiros, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Generating Road Network Graph with Vision-Based Unmanned Vehicle

Yano

Zampirolli

2017

IJURCA

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This work has undergone a double-blind review by a minimum of two faculty members from institutions of higher learning from around the world. The faculty reviewers have expertise in disciplines closely related to those represented by this work. If possible, the work was also reviewed by undergraduates in collaboration with the faculty reviewers. AbstractWith the advancement of technology and its cheapness, robotic vehicles have gained a large number of applications. The spread of their use is growing also because they are getting smaller, lighter and easier to build. In this paper we present a simple and effective way to map a road network with the help of a driverless vehicle. Our approach consists of only three parts: vision-segmentation, angle variation and travelled distance. A video camera attached to a Lego® NXT Mindstorm vehicle guides it by image segmentation using Matlab® Image processing toolbox, along a road network, in which is represented by black tape over a white floor. The algorithm makes the vehicle travel all over the road memorizing main coordinates to identify all crossroads by keeping track of the travelled distance and the current angle. The crossroads and road's end are the nodes of the graph. After several simulations have been performed, the modelling proved to be successful in that small scale approach. Consequently, there are good chances that driverless cars and UAVs also make use of the strategies to map route networks accordingly. The algorithm presented in this paper is useful when there is no localization signal such as GPS, for example, navigation on water, tunnels, inside buildings, among others.

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“…Because of the usefulness of the intelligent vehicle in many applications, research on it is one of the most active fields in the development of science [1]. The controller design for trajectory tracking is a complicated problem, and it is one of the key technologies for the intelligent vehicle.…”

Section: Introductionmentioning

confidence: 99%

An Improved Second Order Sliding Mode Twisting Algorithm for Finite-Time Trajectory Tracking of Intelligent Vehicle

Yang

Wang

2013

Advances in Mechanical Engineering

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This paper proposes a kind of controller comprising Twisting algorithm and adaptive method for the trajectory tracking control of intelligent vehicle system with uncertainties. Firstly, by the adding the adaptive compensation controller, shortcomings of existing second order sliding mode Twisting algorithm are avoided. Second, based on the previosly improved method, we also add another kind of adaptive controller to make the controller parameters do not need to be selected. The intelligent vehicle system can achieve finite-time trajectory tracking, the upper bounds of the uncertainties do not need to be determined previously, and the controller parameters can be adjusted automatically according to the system state. The effectiveness and robustness of the improved algorithm are verified by theory and simulation results of the intelligent vehicle system.

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Autonomous Vehicle Navigation Using Vision and Mapless Strategies: A Survey

Cited by 26 publications

References 46 publications

A perception-driven exploration hierarchical simultaneous localization and mapping for mobile robots adapted to search and rescue environments

A perception-driven exploration hierarchical simultaneous localization and mapping for mobile robots adapted to search and rescue environments

Generating Road Network Graph with Vision-Based Unmanned Vehicle

An Improved Second Order Sliding Mode Twisting Algorithm for Finite-Time Trajectory Tracking of Intelligent Vehicle

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