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

This paper proposes a novel method for appearance based vehicle detection by employing stereo vision system and radar units. In the vein of utilizing advanced driver assistance systems, detection and tracking of moving objects or particularly vehicles, represents an essential task. For the merits of such application, it has often been suggested to combine multiple sensors with complementary modalities. In accordance, in this work we utilize a stereo vision and two radar units, and fuse the corresponding modalities at the level of detection. Firstly, the algorithm executes the detection procedure based on stereo image solely, generating the information about vehicles’ position. Secondly, the final unique list of vehicles is obtained by overlapping the radar readings with the preliminary list obtained by stereo system. The stereo vision–based detection procedure consists of (i) edge processing plugging in also the information about disparity map, (ii) shape based vehicles’ contour extraction and (iii) preliminary vehicles’ positions generation. Since the radar readings are examined by overlapping them with the list obtained by stereo vision, the proposed algorithm can be considered as high level fusion approach. We analyze the performance of the proposed algorithm by performing the real-world experiment in highly dynamic urban environment, under significant illumination influences caused by sunny weather.

Appearance Based Vehicle Detection by Radar-Stereo Vision Integration

In this paper, we present an active SLAM solution with an active loop closing component which is independent on exploration component and at the same time allows high accuracy robot’s pose estimation and complete environment mapping. Inputs to our SLAM algorithm are RGBD image from the Kinect sensor and odometry estimates obtained from inertial measurement unit and wheel encoders. SLAM is based on the exactly sparse delayed state filter for real-time estimation of robot’s trajectory, vision-based pose registration, and loop closing. The active component ensures that localization remains accurate over a long period of time by sending the robot to close loops if a criterion function satisfies the predefined value. Our criterion function depends on the number of states predicted without an update between predictions, information gained from loop closing and the sheer distance between the loop closing state location and the current robot location. Once a state in which a loop closure should occur is reached and an update is performed, the robot returns to its previous goals. Since the active component is independent on the exploration part, the SLAM solution described in this paper can easily be merged with any existing exploration algorithm and the only requirement is that the exploration algorithm is able to stop exploration at any time and continue the exploration after the loop closing was accomplished. In this paper, we propose an active SLAM integration with the 2D laser range finder-based exploration algorithm that ensures the complete coverage of a polygonal environment and therefore a detailed mapping. The developed Active SLAM solution was verified through experiments which demonstrated its capability to work in real-time and to consistently map polygonal environments.

Fast Active SLAM for Accurate and Complete Coverage Mapping of Unknown Environments

Directional data emerge in many scientific disciplines due to the nature of the observed phenomena or the working principles of a sensor. The problem of tracking with direction- only sensors is challenging since the motion of the target typically resides either in 3D or 2D Euclidean space, while the corresponding measurements reside either on the unit sphere or the unit circle, respectively. Furthermore, in multitarget tracking there is the need to deal with the problem of pairing sensors measurements with targets in the presence of clutter (the data association problem). In this paper we propose to approach multitarget tracking in clutter with direction-only data by setting it on the unit hypersphere, thus tracking the objects with a Bayesian estimator based on the von Mises-Fisher distribution and probabilistic data association. To achieve this goal we derive the probabilistic data association (PDA) filter and the joint probabilistic data association (JPDA) filter for the Bayesian von Mises-Fisher estimation on the unit hypersphere. The final PDA and JPDA filter equations are derived with respect to the Kullback-Leibler divergence by preserving the first moment of the hyperspherical distribution. Although the fundamental equations are given for the hyperspherical case, we focus on the filters on the unit 1- sphere (circle in R^2) and the unit 2-sphere (surface of the unit ball in R^3). The proposed approach is validated through synthetic data experiments on 100 Monte Carlo runs simulating multitarget tracking with noisy directional measurements and clutter.

Multitarget tracking with the von Mises-Fisher filter and probabilistic data association

This paper addresses the problem of mobile robot navigation in indoor cluttered environments. A new algorithm for both longitudinal and lateral real-time control of wheel-based mobile robots has been proposed. Its main characteristic is smooth and stable following of the on-line replanned path. Our control method is actually extension of the virtual vehicle method proposed by M. Egerstedt et al., which is based on the introduction of a look-ahead point on the path that serves as reference point for the control algorithm. While the original virtual vehicle method uses only error feedback for reference point movement along the path, our method uses also a feedforward component based on path curve characteristics between the robot and the reference point. In this way stable robot movement is achieved also in the presence of obstacles that are critical with respect to the path following error. Experimental verification is provided for a differential drive robot within an on-line global path planning framework.

A control method for stable and smooth path following of mobile robots

We consider problem of exploration and mapping of unknown indoor environments using laser range finder. We assume a setup with a resolved localization problem and known uncertainty sensor models. Most exploration algorithms are based on detection of a boundary between explored and unexplored regions. They are, however, not efficient in practice due to uncertainties in measurement, localization and map building. The exploration and mapping algorithm is proposed that extends Ekman's exploration algorithm by removing rigid constraints on the range sensor and robot localization. The proposed algorithm includes line extraction algorithm developed by Pfister, which incorporates noise models of the range sensor and robot's pose uncertainty. A line representation of the range data is used for creating polygon that represents explored region from each measurement pose. The polygon edges that do not correspond to real environmental features are candidates for a new measurement pose. A general polygon clipping algorithm is used to obtain the total explored region as the union of polygons from different measurement poses. The proposed algorithm is tested and compared to the Ekman's algorithm by simulations and experimentally on a Pioneer 3DX mobile robot equipped with SICK LMS-200 laser range finder.

/pdf/exploration-and-mapping-of-unknown-polygonal-environments-52ucvyszad.pdf

Ivan Petrović

Papers

Appearance Based Vehicle Detection by Radar-Stereo Vision Integration

Fast Active SLAM for Accurate and Complete Coverage Mapping of Unknown Environments

Multitarget tracking with the von Mises-Fisher filter and probabilistic data association

A control method for stable and smooth path following of mobile robots

Exploration and Mapping of Unknown Polygonal Environments Based on Uncertain Range Data