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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

This paper presents ORB-SLAM, a feature-based monocular simultaneous localization and mapping (SLAM) system that operates in real time, in small and large indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.

/pdf/orb-slam-a-versatile-and-accurate-monocular-slam-system-t5f1c0a74e.pdf

ORB-SLAM: A Versatile and Accurate Monocular SLAM System

This paper presents ORB-SLAM, a feature-based monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public.

/pdf/orb-slam-a-versatile-and-accurate-monocular-slam-system-rnvdngj54a.pdf

ORB-SLAM: a Versatile and Accurate Monocular SLAM System

Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side by side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects.

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

Soft robots are often inspired from biological systems which consist of soft materials or are actuated by electrically activated materials. There are several advantages of soft robots compared to the conventional robots; safe human-machine interaction, adaptability to wearable devices, simple gripping system, and so on. Due to the unique features and advantages, soft robots have a considerable range of applications. This article reviews state-of-the-art researches on soft robots and application areas. Actuation systems for soft robots can be categorized and analyzed into three types: variable length tendon, fluidic actuation, and electro-active polymer (EAP). The deformable property of soft robots restricts the use of many conventional rigid sensors such as encoders, strain gauges, or inertial measurement units. Thus, contactless approaches for sensing and/or sensors with low modulus are preferable for soft robots. Sensors include low modulus (< 1 MPa) elastomers with liquid-phase material filled channels and are appropriate for proprioception which is determined by the degree of curvature. In control perspective, novel control idea should be developed because the conventional control techniques may be inadequate to handle soft robots. Several innovative techniques and diverse materials & fabrication methods are described in this review article. In addition, a wide range of soft robots are characterized and analyzed based on the following sub-categories; actuation, sensing, structure, control and electronics, materials, fabrication and system, and applications.

Soft robot review

This paper presents aerial manipulation using a quadrotor with a two-DOF robot arm. By considering a quadrotor and robot arm as a combined system, the kinematic and dynamic models are developed, and an adaptive sliding mode controller is designed. With the controller, an autonomous flight experiment is conducted including picking up and delivering an object, which requires accurate control of a quadrotor and robot arm. Overall result shows that the proposed approach demonstrates satisfactory performance as a potential platform which can be utilized in various applications such as inspection, manipulation, or transportation in remote places.

/pdf/aerial-manipulation-using-a-quadrotor-with-a-two-dof-robotic-289wwznp3c.pdf

Aerial manipulation using a quadrotor with a two DOF robotic arm

In this paper we describe a vision-based algorithm to control a vertical-takeoff-and-landing unmanned aerial vehicle while tracking and landing on a moving platform. Specifically, we use image-based visual servoing (IBVS) to track the platform in two-dimensional image space and generate a velocity reference command used as the input to an adaptive sliding mode controller. Compared with other vision-based control algorithms that reconstruct a full three-dimensional representation of the target, which requires precise depth estimation, IBVS is computationally cheaper since it is less sensitive to the depth estimation allowing for a faster method to obtain this estimate. To enhance velocity tracking of the sliding mode controller, an adaptive rule is described to account for the ground effect experienced during the maneuver. Finally, the IBVS algorithm integrated with the adaptive sliding mode controller for tracking and landing is validated in an experimental setup using a quadrotor.

H. Jin Kim

Papers

Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter

Soft robot review

Aerial manipulation using a quadrotor with a two DOF robotic arm

Autonomous landing of a VTOL UAV on a moving platform using image-based visual servoing

A flight control system for aerial robots: algorithms and experiments