There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

A Review of Motion Planning Techniques for Automated Vehicles

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

In this paper we present a concept of an assistive robotic system for the people with disabilities. Since the target users most likely have difficulties using standard user interfaces, e.g. keyboard and mouse as input devices for a graphical user interface, we propose braincomputer interface (BCI) to be used for controlling the system. Novelty of our approach lays in the integration of the entire system using an intrabody communication (IBC) network. Using the IBC network reduces the need of superfluous wiring and reduces the time needed to setup the system for use, i.e. it greatly simplifies the use of the system from the user perspective.

Concept of an intrabody networked brain-computer interface controlled assistive robotic system

An adaptive control method based on Takagi-Sugeno fuzzy process model is proposed. It is applicable in cases when the variables in the premises of fuzzy rules, which determine the operating regime of the system, are not measurable. The process dynamics in different operating regimes is described by local linear models, which are combined using fuzzy rules. The premise variables of the fuzzy rules are estimated by minimizing a performance index of the local linear models. The proposed strategy uses the prior knowledge of the process in form of local process models identified offline and stored in the controller's database to simplify the estimation procedure. Thereby, the recursive least-squares identification algorithm used in classic self-tuning control is substituted by much simpler least-squares estimation of a small number of parameters. This makes the proposed method appropriate for implementation on simple platforms, providing, in the same time, the adaptation to changes in operating conditions. The proposed method is experimentally tested on a laboratory liquid level rig. The performance of the proposed control algorithm is compared to the performance of a PI controller.

Adaptive control based on fuzzy process model with estimation of premise variables

Computationally efficient moving object detection and depth estimation from a stereo camera is an extremely useful tool for many computer vision applications, including robotics and autonomous driving. In this paper we show how moving objects can be densely detected by estimating disparity using an algorithm that improves complexity and accuracy of stereo matching by relying on information from previous frames. The main idea behind this approach is that by using the ego-motion estimation and the disparity map of the previous frame, we can set a prior base that enables us to reduce the complexity of the current frame disparity estimation, subsequently also detecting moving objects in the scene. For each pixel we run a Kalman filter that recursively fuses the disparity prediction and reduced space semi-global matching (SGM) measurements. The proposed algorithm has been implemented and optimized using streaming single instruction multiple data instruction set and multi-threading. Furthermore, in order to estimate the process and measurement noise as reliably as possible, we conduct extensive experiments on the KITTI suite using the ground truth obtained by the 3D laser range sensor. Concerning disparity estimation, compared to the OpenCV SGM implementation, the proposed method yields improvement on the KITTI dataset sequences in terms of both speed and accuracy.

Computationally efficient dense moving object detection based on reduced space disparity estimation.

The main task of mobile robots in large environments such as factories, warehouses, and open spaces is to transport goods and people. Planning the path in large environments using classical graph-based search is computationally too intensive. A representation by a hierarchical graph (H-graph) facilitates graph creation and reduces the complexity of path planning. In this paper, we present an algorithm for autonomously generating hierarchy of the environment from floor plans. The hierarchical abstraction depicts the environment in levels, from the most detailed to the most abstract representation of the environment, where pre-computed partial paths at the most detailed level are graph edges in a higher level. We use the E* algorithm to find partial paths in the most detailed abstraction level, and we propose the extraction of higher levels automatically from lower levels. We verified the proposed H-graph creation on our University premises resulting in five abstract levels. 

Autonomous Hierarchy Creation for Path Planning of Mobile Robots in Large Environments

Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to benchmark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the proposed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.

Ivan Petrović

Papers

Concept of an intrabody networked brain-computer interface controlled assistive robotic system

Adaptive control based on fuzzy process model with estimation of premise variables

Computationally efficient dense moving object detection based on reduced space disparity estimation.

Autonomous Hierarchy Creation for Path Planning of Mobile Robots in Large Environments

Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy