Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.

We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system

Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline

In this paper, we show how absolute orientation measurements provided by low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion pipeline. We show that integration improves both runtime, robustness and quality of the 3D reconstruction. In particular, we use this orientation data to seed and regularize the ICP registration technique. We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances. This filter is implemented efficiently on the GPU. Estimating the distribution of the distances helps control the number of iterations necessary for the convergence of the ICP algorithm. Finally, we show experimental results that highlight improvements in robustness, a speed-up of almost 12%, and a gain in tracking quality of 53% for the ATE metric on the Freiburg benchmark.Comment: CVPR Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues 201

Estimation of trailer off-tracking using visual odometry

High Capacity Vehicles (HCVs) have been shown to be highly effective in reducing emissions associated with road freight transport. However, the reduced manoeuvrability of long vehicles often necessitates the use of active trailer steering. Path-following trailer steering systems are very effective in this regard, but are currently limited to on-highway applications due to the manner in which trailer off-tracking is estimated. In this work, a novel trailer off- tracking measurement concept is introduced which is independent of wheel slip and ground surface conditions, and requires no additional sensor measurements or parameter data from the tractor. The concept utilises a stereo camera pair affixed to the trailer and a visual odometry-based algorithm to calculate off-tracking. The concept was evaluated in detailed simulation and full-scale vehicle tests, demonstrating its feasibility and highlighting some important characteristics. RMS measurement errors of 0.11-0.12 m (3.3-3.6%) were obtained in a challenging visual environment.CSIR, South Africa; Cambridge Commonwealth, European and International Trust, UK; Cambridge Vehicle Dynamics Consortium

Real-Time Indoor Localization using Visual and Inertial Odometry

This project encompassed the design of a mobile, real-time localization device for use in an indoor environment. A system was designed and constructed using visual and inertial odometry methods to meet the project requirements. Stereoscopic image features were detected through a C++ Sobel filter implementation and matched. An inertial measurement unit (IMU) provided raw acceleration and rotation coordinates which were transformed into a global frame of reference. A Kalman filter produced motion approximations from the input data and transmitted the Kalman position state coordinates via a radio transceiver to a remote base station. This station used a graphical user interface to map the incoming coordinates

Leveraging External Sensor Data for Enhanced Space Situational Awareness

Reliable Space Situational Awareness (SSA) is a recognized requirement in the current congested, contested, and competitive environment of space operations. A shortage of available sensors and reliable data sources are some current limiting factors for maintaining SSA. Unfortunately, cost constraints prohibit drastically increasing the sensor inventory. Alternative methods are sought to enhance current SSA, including utilizing non-traditional data sources (external sensors) to perform basic SSA catalog maintenance functions. Astronomical data, for example, routinely collects serendipitous satellite streaks in the course of observing deep space; but tactics, techniques, and procedures designed to glean useful information from those collects have yet to be rigorously developed. This work examines the feasibility and utility of performing ephemeris positional updates for a Resident Space Object (RSO) catalog using metric data obtained from RSO streaks gathered by astronomical telescopes. The focus of this work is on processing data from three possible streak categories: streaks that only enter, only exit, or cross completely through the astronomical image. Successful use of this data will aid in resolving uncorrelated tracks, space object identification, and threat detection. Incorporation of external data sources will also reduce the number of routine collects required by existing SSA sensors, freeing them up for more demanding tasks. The results clearly demonstrate that accurate orbital reconstruction can be performed using an RSO streak in a distorted image, without applying calibration frames and that partially bound streaks provide similar results to traditional data, with a mean degradation of 6:2% in right ascension and 42:69% in declination. The methodology developed can also be applied to dedicated SSA sensors to extract data from serendipitous streaks gathered while observing other RSOs