408 research outputs found
Single and multiple stereo view navigation for planetary rovers
© Cranfield UniversityThis thesis deals with the challenge of autonomous navigation of the ExoMars rover.
The absence of global positioning systems (GPS) in space, added to the limitations
of wheel odometry makes autonomous navigation based on these two techniques - as
done in the literature - an inviable solution and necessitates the use of other approaches.
That, among other reasons, motivates this work to use solely visual data to solve the
robot’s Egomotion problem.
The homogeneity of Mars’ terrain makes the robustness of the low level image
processing technique a critical requirement. In the first part of the thesis, novel solutions
are presented to tackle this specific problem. Detection of robust features against
illumination changes and unique matching and association of features is a sought after
capability. A solution for robustness of features against illumination variation is proposed
combining Harris corner detection together with moment image representation.
Whereas the first provides a technique for efficient feature detection, the moment images
add the necessary brightness invariance. Moreover, a bucketing strategy is used
to guarantee that features are homogeneously distributed within the images. Then, the
addition of local feature descriptors guarantees the unique identification of image cues.
In the second part, reliable and precise motion estimation for the Mars’s robot is
studied. A number of successful approaches are thoroughly analysed. Visual Simultaneous
Localisation And Mapping (VSLAM) is investigated, proposing enhancements
and integrating it with the robust feature methodology. Then, linear and nonlinear optimisation
techniques are explored. Alternative photogrammetry reprojection concepts
are tested. Lastly, data fusion techniques are proposed to deal with the integration of
multiple stereo view data.
Our robust visual scheme allows good feature repeatability. Because of this,
dimensionality reduction of the feature data can be used without compromising the
overall performance of the proposed solutions for motion estimation. Also, the developed
Egomotion techniques have been extensively validated using both simulated and
real data collected at ESA-ESTEC facilities. Multiple stereo view solutions for robot
motion estimation are introduced, presenting interesting benefits. The obtained results
prove the innovative methods presented here to be accurate and reliable approaches
capable to solve the Egomotion problem in a Mars environment
Development and localization of a mobile robot
An autonomous mobile robot needs to have the capability to locate itself within an environment, besides the ability to avoid any obstacles during its movement. This paper covers the development of an autonomous mobile robot. It was equipped with two ultrasonic sensors for obstacle avoidance purposes. The mobile robot was tested within a static environment. The results show that the robot is capable of navigating itself in the defined environment without being provided with a prior map. In summary, the mobile robot was successfully developed, and its localization capability was achieved
Enriching remote labs with computer vision and drones
165 p.With the technological advance, new learning technologies are being developed in order to contribute to better learning experience. In particular, remote labs constitute an interesting and a practical way that can motivate nowadays students to learn. The studen can at anytime, and from anywhere, access the remote lab and do his lab-work. Despite many advantages, remote tecnologies in education create a distance between the student and the teacher. Without the presence of a teacher, students can have difficulties, if no appropriate interventions can be taken to help them. In this thesis, we aim to enrich an existing remote electronic lab made for engineering students called "LaboREM" (for remote Laboratory) in two ways: first we enable the student to send high level commands to a mini-drone available in the remote lab facility. The objective is to examine the front panels of electronic measurement instruments, by the camera embedded on the drone. Furthermore, we allow remote student-teacher communication using the drone, in case there is a teacher present in the remote lab facility. Finally, the drone has to go back home when the mission is over to land on a platform for automatic recharge of the batteries. Second, we propose an automatic system that estimates the affective state of the student (frustrated/confused/flow) in order to take appropriate interventions to ensure good learning outcomes. For example, if the studen is having major difficulties we can try to give him hints or to reduce the difficulty level of the lab experiment. We propose to do this by using visual cues (head pose estimation and facil expression analysis). Many evidences on the state of the student can be acquired, however these evidences are incomplete, sometims inaccurate, and do not cover all the aspects of the state of the student alone. This is why we propose to fuse evidences using the theory of Dempster-Shafer that allows the fusion of incomplete evidence
Constructing informative Bayesian priors to improve SLAM map quality
The problem of Simultaneous Localisation And Mapping (SLAM) has been widely researched and has been of particular interest in recent years, with robots and self driving cars becoming ubiquitous. SLAM solutions to date have aimed to produce faster, more robust solutions that yield consistent maps by improving the filtering algorithms used, introducing better sensors, more efficient map representations or improved motion estimates. Whilst performing well in simplified scenarios, many of these solutions perform poorly in challenging real life scenarios. It is therefore important to produce SLAM solutions that can perform well even when using limited computational resources and performing a quick exploration for time critical operations such as Urban Search And Rescue missions. In order to address this problem this thesis proposes the construction of informative Bayesian priors to improve performance without adding to the computational complexity of the SLAM algorithm. Indoors occupancy grid SLAM is used as a case study to demonstrate this concept and architectural drawings are used as a source of prior information. The use of prior information to improve the performance of robotics systems has been successful in applications such as visual odometry, self-driving car navigation and object recognition. However, none of these solutions leverage prior information to construct Bayesian priors that can be used in recursive map estimation.
This thesis addresses this problem and proposes a novel method to process architectural drawings and floor plans to extract structural information. A study is then conducted to identify optimal prior values of occupancy to assign to extracted walls and empty space. A novel approach is proposed to assess the quality of maps produced using different priors and a multi-objective optimisation is used to identify Pareto optimal values. The proposed informative priors are found to perform better than the commonly used non-informative prior, yielding an increase of over 20% in the F2 metric, without adding to the computational complexity of the SLAM algorithm
Enriching remote labs with computer vision and drones
165 p.With the technological advance, new learning technologies are being developed in order to contribute to better learning experience. In particular, remote labs constitute an interesting and a practical way that can motivate nowadays students to learn. The studen can at anytime, and from anywhere, access the remote lab and do his lab-work. Despite many advantages, remote tecnologies in education create a distance between the student and the teacher. Without the presence of a teacher, students can have difficulties, if no appropriate interventions can be taken to help them. In this thesis, we aim to enrich an existing remote electronic lab made for engineering students called "LaboREM" (for remote Laboratory) in two ways: first we enable the student to send high level commands to a mini-drone available in the remote lab facility. The objective is to examine the front panels of electronic measurement instruments, by the camera embedded on the drone. Furthermore, we allow remote student-teacher communication using the drone, in case there is a teacher present in the remote lab facility. Finally, the drone has to go back home when the mission is over to land on a platform for automatic recharge of the batteries. Second, we propose an automatic system that estimates the affective state of the student (frustrated/confused/flow) in order to take appropriate interventions to ensure good learning outcomes. For example, if the studen is having major difficulties we can try to give him hints or to reduce the difficulty level of the lab experiment. We propose to do this by using visual cues (head pose estimation and facil expression analysis). Many evidences on the state of the student can be acquired, however these evidences are incomplete, sometims inaccurate, and do not cover all the aspects of the state of the student alone. This is why we propose to fuse evidences using the theory of Dempster-Shafer that allows the fusion of incomplete evidence
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