Efficient Visual Ego-Motion Estimation for Agile Flying Robots

Micro air vehicles (MAVs) have shown significant potential in modern society. The development in robotics and automation is changing the roles of MAVs from remotely controlled machines requiring human pilots to autonomous and intelligent robots. There is an increasing number of autonomous MAVs involved in outdoor operations. In contrast, the...

DeepEgo: Deep Instantaneous Ego-Motion Estimation Using Automotive Radar

The problem of instantaneous ego-motion estimation with mm-wave automotive radar is studied. DeepEgo, a deep learning-based method, is proposed for achieving robust and accurate ego-motion estimation. A hybrid approach that uses neural networks to extract complex features from input point clouds and applies weighted least squares (WLS) for...

3-D Ego-Motion Estimation Using Multi-Channel FMCW Radar

The problem of estimating the 3D ego-motion velocity using multi-channel FMCW radar sensors has been studied. For the first time, the problem of ego-motion estimation is treated using radar raw signals. A robust algorithm using multi-channel FMCW radar sensors to instantly determine the complete 3D motion state of the ego-vehicle (i.e.,...

Radar-only Instantaneous Ego-motion Estimation Using Neural Networks

The problem of 2D instantaneous ego-motion estimation for vehicles equipped with automotive radars is studied. To leverage multi-dimensional radar point clouds and exploit point features automatically, without human engineering, a novel approach is proposed that transforms ego-motion estimation into a weighted least squares (wLSQ) problem using...

Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles Using Feedforward Neural Networks

In this paper, an approach for reducing the drift in monocular visual odometry algorithms is proposed based on a feedforward neural network. A visual odometry algorithm computes the incremental motion of the vehicle between the successive camera frames, then integrates these increments to determine the pose of the vehicle. The proposed neural...

Observability of the relative motion from inertial data in kinematic chains

Real-time motion tracking of kinematic chains is a key prerequisite in the control of, e.g., robotic actuators and autonomous vehicles and also has numerous biomechanical applications. In recent years, it has been shown that, by placing inertial sensors on segments that are connected by rotational joints, the motion of that kinematic chain...

A novel encoding element for robust pose estimation using planar fiducials

Pose estimation in robotics is often achieved using images from known and purposefully applied markers or fiducials taken by a monocular camera. This low-cost system architecture can provide accurate and precise pose estimation measurements. However, to prevent the restriction of robotic movement and occlusions of features, the fiducial markers...

A Framework for Indoor Localization Using the Magnetic Field

In this work, our focus is on indoor localization using the indoor magnetic field as a source of position information. This relies on the fact that ferromagnetic materials inside buildings cause the magnetic field to vary spatially. We jointly estimate the pose of a combined sensor module (containing a magnetometer) as well as the magnetic field...

Sparse Magnetometer-free Inertial Motion Tracking - A Condition for Observability in Double Hinge Joint Systems

Inertial measurement units are commonly used in a growing number of application fields to track or capture motions of kinematic chains, such as human limbs, exoskeletons or robotic actuators. A major challenge is the presence of magnetic disturbances that results in unreliable magnetometer readings. Recent research revealed that this problem...

Inertial sensors—applications and challenges in a nutshell

This editorial provides a concise introduction to the methods and applications of inertial sensors. We briefly describe the main characteristics of inertial sensors and highlight the broad range of applications as well as the methodological challenges. Finally, for the reader’s guidance, we give a succinct overview of the papers included in...

A Sensor Data Fusion Algorithm for Human Motion Estimation

In the elderly population, falls are one of the major causes of injuries. Fall detection algorithms for wearable devices in the literature were found to focus on differentiating Activities of Daily Living (ADL) from falls, rather than on early fall detection and prevention before impact. This thesis work was aimed at providing accurate estimates...

Distributed Video Coding (DVC): Motion estimation and DCT quantization in low complexity video compression

The main focus of video encoding in the past twenty years has been on video broadcasting. A video is captured and encoded by professional equipment and then watched on varying consumer devices. Consequently, the focus was to increase the quality and to keep down the decoder complexity. In more recent years we observe a shift in user behavior,...

Attitude estimation for low-cost spacecraft equipped with GPS

The objective of the present paper is to give an overview of spacecraft attitude determination with GPS. GPS receivers with single or multiple antennas nowadays are low-cost sensors that constitute a valuable complement to existing sensor suites. GPS has been widely used for positioning and vehicle navigation. However, using GPS as an attitude...