The intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that1were founded by the Association for Unmanned Vehicle Systems International (AUVSI). The IGVC is a multidisc...
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The computational framework, based on the conformal camera, is developedfor processingvisual information during smooth pursuit movements of a robotic eye. During smooth pursuit, the image of the tracked object remains...
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In this paper, we design an autonomous flight controller for height regulation and bang-bang controller for directional control of a light-weight flapping-wing micro air vehicle (FWMAV) with limited payload. We also p...
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ISBN:
(纸本)9781509037636
In this paper, we design an autonomous flight controller for height regulation and bang-bang controller for directional control of a light-weight flapping-wing micro air vehicle (FWMAV) with limited payload. We also present autonomous vision-based target tracking for a FWMAV equipped with a low-cost and light-weight first person view (FPV) camera. We construct a ground station, integrated with control and visionalgorithms, which performs the image processing and the computation of control inputs based on the acquired state variables from motion capture system. In addition, we employ a vision algorithm for a low-quality camera to detect a static target with the discussions on the techniques to improve the reliability of visual detection. Experimental results show satisfactory flight performance, achieving the height regulation and directional control, and autonomous vision-based target tracking.
Obstacle detection and localization behaviours have shown to work robustly in 2D perceived environment. With the progress of proximity sensors, precisely the emergence of 3D visiontechniques, it would be interesting ...
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Obstacle detection and localization behaviours have shown to work robustly in 2D perceived environment. With the progress of proximity sensors, precisely the emergence of 3D visiontechniques, it would be interesting to examine these motion controllers whether for 3D perceived environment. 2D basic algorithms for obstacle avoidance or robot location, needs reformulation in order to process data from 3D perception devices. In this work, we introduce a 3D obstacle detection controller, combined with particle filter localization. The obstacle detection control described in this paper address 3D obstacles with different shapes and the particle filter uses 3D environment data to correct the wheelchair localization based on his kinematics model.
Spherical stereo vision is a kind of stereo vision system built by fish-eye lenses, which discussing the stereo algorithms conform to the spherical model. Epipolar geometry is the theory which describes the relationsh...
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ISBN:
(纸本)9781628414967
Spherical stereo vision is a kind of stereo vision system built by fish-eye lenses, which discussing the stereo algorithms conform to the spherical model. Epipolar geometry is the theory which describes the relationship of the two imaging plane in cameras for the stereo vision system based on perspective projection model. However, the epipolar in uncorrected fish-eye image will not be a line but an arc which intersects at the poles. It is polar curve. In this paper, the theory of nonlinear epipolar geometry will be explored and the method of nonlinear epipolar rectification will be proposed to eliminate the vertical parallax between two fish-eye images. Maximally Stable Extremal Region (MSER) utilizes grayscale as independent variables, and uses the local extremum of the area variation as the testing results. It is demonstrated in literatures that MSER is only depending on the gray variations of images, and not relating with local structural characteristics and resolution of image. Here, MSER will be combined with the nonlinear epipolar rectification method proposed in this paper. The intersection of the rectified epipolar and the corresponding MSER region is determined as the feature set of spherical stereo vision. Experiments show that this study achieved the expected results.
This paper presents improvements made to the intelligence algorithms employed on Q, an autonomous ground vehicle, for the 2014 intelligent Ground Vehicle Competition (IGVC). In 2012, the IGVC committee combined the fo...
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ISBN:
(纸本)9781628414967
This paper presents improvements made to the intelligence algorithms employed on Q, an autonomous ground vehicle, for the 2014 intelligent Ground Vehicle Competition (IGVC). In 2012, the IGVC committee combined the formerly separate autonomous and navigation challenges into a single AUT-NAV challenge. In this new challenge, the vehicle is required to navigate through a grassy obstacle course and stay within the course boundaries (a lane of two white painted lines) that guide it toward a given GPS waypoint. Once the vehicle reaches this waypoint, it enters an open course where it is required to navigate to another GPS waypoint while avoiding obstacles. After reaching the final waypoint, the vehicle is required to traverse another obstacle course before completing the run. Q uses modular parallel software architecture in which image processing, navigation, and sensor control algorithms run concurrently. A tuned navigation algorithm allows Q to smoothly maneuver through obstacle fields. For the 2014 competition, most revisions occurred in the vision system, which detects white lines and informs the navigation component. Barrel obstacles of various colors presented a new challenge for image processing: the previous color plane extraction algorithm would not suffice. To overcome this difficulty, laser range sensor data were overlaid on visual data. Q also participates in the Joint Architecture for Unmanned Systems (JAUS) challenge at IGVC. For 2014, significant updates were implemented: the JAUS component accepted a greater variety of messages and showed better compliance to the JAUS technical standard. With these improvements, Q secured second place in the JAUS competition.
Fish-eye lens is a kind of short focal distance (f=6 similar to 16mm) camera. The field of view (FOV) of it is near or even exceeded 180x180 degrees. A lot of literatures show that the multiple view geometry system bu...
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ISBN:
(纸本)9781628414967
Fish-eye lens is a kind of short focal distance (f=6 similar to 16mm) camera. The field of view (FOV) of it is near or even exceeded 180x180 degrees. A lot of literatures show that the multiple view geometry system built by fish-eye lens will get larger stereo field than traditional stereo vision system which based on a pair of perspective projection images. Since a fish-eye camera usually has a wider-than-hemispherical FOV, the most of image processing approaches based on the pinhole camera model for the conventional stereo vision system are not satisfied to deal with the applications of this category of stereo vision which built by fish-eye lenses. This paper focuses on discussing the calibration and the epipolar rectification method for a novel machine vision system set up by four fish-eye lenses, which is called Special Stereo vision System (SSVS). The characteristic of SSVS is that it can produce 3D coordinate information from the whole global observation space and acquiring no blind area 360 degrees x360 degrees panoramic image simultaneously just using single vision equipment with one time static shooting. Parameters calibration and epipolar rectification is the basic for SSVS to realize 3D reconstruction and panoramic image generation.
In this paper, we present an enhanced loop closure method* based on image-to-image matching relies on quantized local Zernike moments. In contradistinction to the previous methods, our approach uses additional depth i...
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ISBN:
(纸本)9781628414967
In this paper, we present an enhanced loop closure method* based on image-to-image matching relies on quantized local Zernike moments. In contradistinction to the previous methods, our approach uses additional depth information to extract Zernike moments in local manner. These moments are used to represent holistic shape information inside the image. The moments in complex space that are extracted from both grayscale and depth images are coarsely quantized. In order to find out the similarity between two locations, nearest neighbour (NN) classification algorithm is performed. Exemplary results and the practical implementation case of the method are also given with the data gathered on the testbed using a Kinect. The method is evaluated in three different datasets of different lighting conditions. Additional depth information with the actual image increases the detection rate especially in dark environments. The results are referred as a successful, high-fidelity online method for visual place recognition as well as to close navigation loops, which is a crucial information for the well known simultaneously localization and mapping (SLAM) problem. This technique is also practically applicable because of its low computational complexity, and performing capability in real-time with high loop closing accuracy.
Underwater environments present a considerable challenge for computervision, since water is a scattering medium with substantial light absorption characteristics which is made even more severe by turbidity. This pose...
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