Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

High-Precision Localization Using Ground Texture

Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local keypoints in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces

Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review

The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools

Human robot interaction in a crowded environment

Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3]. Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person. Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability

Motion Conflict Detection and Resolution in Visual-Inertial Localization Algorithm

In this dissertation, we have focused on conflicts that occur due to disagreeing motions in multi-modal localization algorithms. In spite of the recent achievements in robust localization by means of multi-sensor fusion, these algorithms are not applicable to all environments. This is primarily attributed to the following fundamental assumptions: (i) the environment is predominantly stationary, (ii) only ego-motion of the sensor platform exists, and (iii) multiple sensors are always in agreement with each other regarding the observed motion. Recently, studies have shown how to relax the static environment assumption using outlier rejection techniques and dynamic object segmentation. Additionally, to handle non ego-motion, approaches that extend the localization algorithm to multi-body tracking have been studied. However, there has been no attention given to the conditions where multiple sensors contradict each other with regard to the motions observed. Vision based localization has become an attractive approach for both indoor and outdoor applications due to the large information bandwidth provided by images and reduced cost of the cameras used. In order to improve the robustness and overcome the limitations of vision, an Inertial Measurement Unit (IMU) may be used. Even though visual-inertial localization has better accuracy and improved robustness due to the complementary nature of camera and IMU sensor, they are affected by disagreements in motion observations. We term such dynamic situations as environments with motion conflictbecause these are caused when multiple different but self- consistent motions are observed by different sensors. Tightly coupled visual inertial fusion approaches that disregard such challenging situations exhibit drift that can lead to catastrophic errors. We have provided a probabilistic model for motion conflict. Additionally, a novel algorithm to detect and resolve motion conflicts is also presented. Our method to detect motion conflicts is based on per-frame positional estimate discrepancy and per- landmark reprojection errors. Motion conflicts were resolved by eliminating inconsistent IMU and landmark measurements. Finally, a Motion Conflict aware Visual Inertial Odometry (MC- VIO) algorithm that combined both detection and resolution of motion conflict was implemented. Both quantitative and qualitative evaluation of MC-VIO on visually and inertially challenging datasets were obtained. Experimental results indicated that MC-VIO algorithm reduced the absolute trajectory error by 70% and the relative pose error by 34% in scenes with motion conflict, in comparison to the reference VIO algorithm. Motion conflict detection and resolution enables the application of visual inertial localization algorithms to real dynamic environments. This paves the way for articulate object tracking in robotics. It may also find numerous applications in active long term augmented reality