Design and calibration of a specialized polydioptric camera rig

The development of advanced computational machines does not necessarily provide solutions to all the scientific problems in the research. It has been observed in the nature that all creatures have evolved highly exclusive sensory organs depending on their habitat and the form of availability of the resources they utilize for their survival. In this project, a novel omnidirectional camera rig is proposed that is exclusively designed to operate for highly specified operations and tasks in the field of mobile robots. Navigation problems on uneven terrains and detection of the moving objects while the robot is itself in motion are the core problems that omnidirectional systems tackle. The proposed omnidirectional system is a compact and a rigid vision system with dioptric cameras that provide a 360° field-of-view in horizontal and vertical, with no blind spot in their site plus a high resolution stereo camera is mounted to monitor anterior field-of-view for precise results with depth information of the scene. Structure from motion algorithm is adapted and implemented to prove the validity of the design of the proposed camera rig and a toolbox is developed to calibrate similar systems

Development of a Real-Time Detection System for Augmented Reality Driving

Augmented reality technology is applied so that driving tests may be performed in various environments using a virtual reality scenario with the ultimate goal of improving visual and interactive effects of simulated drivers. Environmental conditions simulating a real scenario are created using an augmented reality structure, which guarantees the test taker’s security since they are not subject to real-life elements and dangers. Furthermore, the accuracy of tests conducted through virtual reality is not influenced by either environmental or human factors. Driver posture is captured in real time using Kinect’s depth perception function and then applied to driving simulation effects that are emulated by Unity3D’s gaming technology. Subsequently, different driving models may be collected through different drivers. In this research, nearly true and realistic street environments are simulated to evaluate driver behavior. A variety of different visual effects are easily available to effectively reduce error rates, thereby significantly improving test security as well as the reliability and reality of this project. Different situation designs are simulated and evaluated to increase development efficiency and build more security verification test platforms using such technology in conjunction with driving tests, vehicle fittings, environmental factors, and so forth

Disparity Map Generation from Illumination Variant Stereo Images Using Efficient Hierarchical Dynamic Programming

A novel hierarchical stereo matching algorithm is presented which gives disparity map as output from illumination variant stereo pair. Illumination difference between two stereo images can lead to undesirable output. Stereo image pair often experience illumination variations due to many factors like real and practical situation, spatially and temporally separated camera positions, environmental illumination fluctuation, and the change in the strength or position of the light sources. Window matching and dynamic programming techniques are employed for disparity map estimation. Good quality disparity map is obtained with the optimized path. Homomorphic filtering is used as a preprocessing step to lessen illumination variation between the stereo images. Anisotropic diffusion is used to refine disparity map to give high quality disparity map as a final output. The robust performance of the proposed approach is suitable for real life circumstances where there will be always illumination variation between the images. The matching is carried out in a sequence of images representing the same scene, however in different resolutions. The hierarchical approach adopted decreases the computation time of the stereo matching problem. This algorithm can be helpful in applications like robot navigation, extraction of information from aerial surveys, 3D scene reconstruction, and military and security applications. Similarity measure SAD is often sensitive to illumination variation. It produces unacceptable disparity map results for illumination variant left and right images. Experimental results show that our proposed algorithm produces quality disparity maps for both wide range of illumination variant and invariant stereo image pair

Mobile Robot Tracking with Deep Learning Models under the Specific Environments

Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design.</jats:p

Wide-Angle Camera Distortions And Non-Uniform Illumination In Mobile Robot Tracking

In this paper some fundamentals and solutions to accompanying problems in vision system design for mobile robot tracking are presented. The main topics are correction of camera lens distortion and compensation of non-uniform illumination. Both correction methods contribute to vision system performance if implemented in the appropriate manner. Their applicability is demonstrated by applying them to vision for robot soccer. The lens correction method successfully corrects the distortion caused by the camera lens, thus achieving a more accurate and precise estimation of object position. The illumination compensation improves robustness to irregular and non-uniform illumination that is nearly always present in real conditions. 2003 Elsevier B.V. All rights reserved