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

We present a novel approach to automatic indoor scene reconstruction from RGB-D images acquired from a single viewpoint using active vision. The proposed method is designed to select the next view ...

Active vision for 3D indoor scene reconstruction using a 3D camera on a pan-tilt mechanism

The paper deals with development of platform based, embedded, hard real-time systems for traction and power engineering applications. Such systems are intended to have lifecyle of more than 20 years. Several cases of successful projects based on two typical platform architectures are presented. Both hardware and software platforms and components are described along with some topics that can help to identify advantages and disadvantages of such an attitude. Particular attention is given to the problem usually not taken into account-component obsolescence.

Platform based development of embedded systems for traction and power engineering applications - experiences and challenges

Simultaneous localization and mapping (SLAM) is a core element of every autonomous mobile robot. The underlying engine of a SLAM system is its back-end, which aims at optimally estimating the trajectory and map of the environment based on sensor data abstractions. Over the past decade, SLAM solutions based on graph optimization approaches prevailed over the filtering based solutions, since they dominated in performance over a wider range of applications. In this paper we propose a novel filtering based SLAM back-end based on the exactly sparse delayed state filter (ESDSF) derived on Lie groups (LG-ESDSF). The proposed filter retains all the good characteristics of the classic ESDSF, but also respects the state space geometry by employing filtering equations directly on Lie groups. We have compared our SLAM system with two current state-of-the-art SLAM solutions, namely ORB-SLAM and LSD-SLAM, on the KITTI vision benchmark suite. Test results show that the proposed SLAM based on the LG-ESDSF back-end can achieve same level of accuracy as the methods based on the graph optimization techniques, while maintaining lower computation times.

Revival of filtering based SLAM? Exactly sparse delayed state filter on Lie groups

Mobile robots equipped with an omnidirectional camera have gained a considerable attention over the last decade. Having an entire view of the scene can be very advantageous in numerous applications as all information is stored in a single frame. This paper is primarily concerned with detection of moving objects from optical flow field in cluttered indoor environments, which is necessary for safe navigation and collision avoidance. The algorithm is based on the comparison of the measured optical flow vectors with the generated ones. As depth information is not available, a novel method is proposed which iteratively generates optical flow vectors for different possible real world coordinates of the objects in the scene. This is necessary in order to incorporate motion estimates given by motor encoders. Back-projecting into image is then used to generate synthetic optical flow vectors needed for comparison. The algorithm was tested on a real system and was able to successfully localize a moving object under both artificial and natural lighting. The proposed algorithm can be implemented in real-time on any system with known calibrated model of the omnidirectional sensor and reliable motion estimation.

Real-time detection of moving objects by a mobile robot with an omnidirectional camera

Operation of traction drive and auxiliary power supply converters installed onboard modern vehicles causes increased content of line harmonics and interharmonic components in the line current. Passive techniques for filtration of low frequency interference line currents appeared to be insufficient, but are combined with active mitigation techniques. The paper describes and suggests active suppression methods based on proper design of the grid interface of the power converter and its corresponding control system. Interference currents, including those induced by the operation of onboard power converters can significantly influence the correct operation of telecommunication, train control systems and other railway signaling infrastructure along the tracks, having direct impact to the safety of the railway transport. Therefore, suppression of low-frequency interference line currents requires careful implementation of active/passive methods. For purpose of quality evaluation of performances of the grid interface of the traction drive converters and its corresponding control system in particular, a series of tests for assessment of the line current frequency spectrum, resulting from the operation of the converter in typical exploitation conditions, were performed. Tests were performed in laboratory conditions and on the vehicle, with satisfactory results.

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Ivan Petrović

Papers

Active vision for 3D indoor scene reconstruction using a 3D camera on a pan-tilt mechanism

Platform based development of embedded systems for traction and power engineering applications - experiences and challenges

Revival of filtering based SLAM? Exactly sparse delayed state filter on Lie groups

Real-time detection of moving objects by a mobile robot with an omnidirectional camera

Active Suppression of Low-frequency Interference Currents by Implementation of the High-performance Control System for the Grid-interfaced Converters