Vision-Aided Inertial Navigation

This document discloses, among other things, a system and method for implementing an algorithm to determine pose, velocity, acceleration or other navigation information using feature tracking data. The algorithm has computational complexity that is linear with the number of features tracked

RTLola Cleared for Take-Off: Monitoring Autonomous Aircraft

The autonomous control of unmanned aircraft is a highly safety-critical domain with great economic potential in a wide range of application areas, including logistics, agriculture, civil engineering, and disaster recovery. We report on the development of a dynamic monitoring framework for the DLR ARTIS (Autonomous Rotorcraft Testbed for Intelligent Systems) family of unmanned aircraft based on the formal specification language RTLola. RTLola is a stream-based specification language for real-time properties. An RTLola specification of hazardous situations and system failures is statically analyzed in terms of consistency and resource usage and then automatically translated into an FPGA-based monitor. Our approach leads to highly efficient, parallelized monitors with formal guarantees on the noninterference of the monitor with the normal operation of the autonomous system

Inertial navigation aided by simultaneous loacalization and mapping

Unmanned aerial vehicles technologies are getting smaller and cheaper to use and the challenges of payload limitation in unmanned aerial vehicles are being overcome. Integrated navigation system design requires selection of set of sensors and computation power that provides reliable and accurate navigation parameters (position, velocity and attitude) with high update rates and bandwidth in small and cost effective manner. Many of today’s operational unmanned aerial vehicles navigation systems rely on inertial sensors as a primary measurement source. Inertial Navigation alone however suffers from slow divergence with time. This divergence is often compensated for by employing some additional source of navigation information external to Inertial Navigation. From the 1990’s to the present day Global Positioning System has been the dominant navigation aid for Inertial Navigation. In a number of scenarios, Global Positioning System measurements may be completely unavailable or they simply may not be precise (or reliable) enough to be used to adequately update the Inertial Navigation hence alternative methods have seen great attention. Aiding Inertial Navigation with vision sensors has been the favoured solution over the past several years. Inertial and vision sensors with their complementary characteristics have the potential to answer the requirements for reliable and accurate navigation parameters. In this thesis we address Inertial Navigation position divergence. The information for updating the position comes from combination of vision and motion. When using such a combination many of the difficulties of the vision sensors (relative depth, geometry and size of objects, image blur and etc.) can be circumvented. Motion grants the vision sensors with many cues that can help better to acquire information about the environment, for instance creating a precise map of the environment and localize within the environment. We propose changes to the Simultaneous Localization and Mapping augmented state vector in order to take repeated measurements of the map point. We show that these repeated measurements with certain manoeuvres (motion) around or by the map point are crucial for constraining the Inertial Navigation position divergence (bounded estimation error) while manoeuvring in vicinity of the map point. This eliminates some of the uncertainty of the map point estimates i.e. it reduces the covariance of the map points estimates. This concept brings different parameterization (feature initialisation) of the map points in Simultaneous Localization and Mapping and we refer to it as concept of aiding Inertial Navigation by Simultaneous Localization and Mapping. We show that making such an integrated navigation system requires coordination with the guidance and control measurements and the vehicle task itself for performing the required vehicle manoeuvres (motion) and achieving better navigation accuracy. This fact brings new challenges to the practical design of these modern jam proof Global Positioning System free autonomous navigation systems. Further to the concept of aiding Inertial Navigation by Simultaneous Localization and Mapping we have investigated how a bearing only sensor such as single camera can be used for aiding Inertial Navigation. The results of the concept of Inertial Navigation aided by Simultaneous Localization and Mapping were used. New parameterization of the map point in Bearing Only Simultaneous Localization and Mapping is proposed. Because of the number of significant problems that appear when implementing the Extended Kalman Filter in Inertial Navigation aided by Bearing Only Simultaneous Localization and Mapping other algorithms such as Iterated Extended Kalman Filter, Unscented Kalman Filter and Particle Filters were implemented. From the results obtained, the conclusion can be drawn that the nonlinear filters should be the choice of estimators for this application

Pilot Assisted Inertial Navigation System Aiding Using Bearings-Only Measurements Taken Over Time

The objective of this work is to develop an alternative INS aiding source other than the GPS, while preserving the autonomy of the integrated navigation system. It is proposed to develop a modernized method of aerial navigation using driftmeter measurements from an E/O system for ground feature tracking, and an independent altitude sensor in conjunction with the INS. The pilot will track a ground feature with the E/O system, while the aircraft is on autopilot holding constant airspeed, altitude, and heading during an INS aiding session. The ground feature measurements from the E/O system and the INS output form measurements provided to a linear KF running on the navigation computer to accomplish the INS aiding action. Aiding the INS will be periodically repeated as operationally permissible under pilot discretion. Little to no modeling error will be present when implementing the linear Kalman filter, indicating the strength of the INS aiding action will be exclusively determined by the prevailing degree of observability

Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%

Autonomous aerial robot for high-speed search and intercept applications

In recent years, high-speed navigation and environment interaction in the context of aerial robotics has become a field of interest for several academic and industrial research studies. In particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research area due to their potential usability in several environments. Nevertheless, SaI tasks involve a challenging development regarding sensory weight, onboard computation resources, actuation design, and algorithms for perception and control, among others. In this work, a fully autonomous aerial robot for high-speed object grasping has been proposed. As an additional subtask, our system is able to autonomously pierce balloons located in poles close to the surface. Our first contribution is the design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with additional sensors enabling the robot to grasp objects at high speeds. The second contribution is a complete software framework consisting of perception, state estimation, motion planning, motion control, and mission control in order to rapidly and robustly perform the autonomous grasping mission. Our approach has been validated in a challenging international competition and has shown outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Airborne Navigation by Fusing Inertial and Camera Data

Unmanned aircraft systems (UASs) are often used as measuring system. Therefore, precise knowledge of their position and orientation are required. This thesis provides research in the conception and realization of a system which combines GPS-assisted inertial navigation systems with the advances in the area of camera-based navigation. It is presented how these complementary approaches can be used in a joint framework. In contrast to widely used concepts utilizing only one of the two approaches, a more robust overall system is realized. The presented algorithms are based on the mathematical concepts of rigid body motions. After derivation of the underlying equations, the methods are evaluated in numerical studies and simulations. Based on the results, real-world systems are used to collect data, which is evaluated and discussed. Two approaches for the system calibration, which describes the offsets between the coordinate systems of the sensors, are proposed. The first approach integrates the parameters of the system calibration in the classical bundle adjustment. The optimization is presented very descriptive in a graph based formulation. Required is a high precision INS and data from a measurement flight. In contrast to classical methods, a flexible flight course can be used and no cost intensive ground control points are required. The second approach enables the calibration of inertial navigation systems with a low positional accuracy. Line observations are used to optimize the rotational part of the offsets. Knowledge of the offsets between the coordinate systems of the sensors allows transforming measurements bidirectional. This is the basis for a fusion concept combining measurements from the inertial navigation system with an approach for the visual navigation. As a result, more robust estimations of the own position and orientation are achieved. Moreover, the map created from the camera images is georeferenced. It is shown how this map can be used to navigate an unmanned aerial system back to its starting position in the case of a disturbed or failed GPS reception. The high precision of the map allows the navigation through previously unexplored area by taking into consideration the maximal drift for the camera-only navigation. The evaluated concept provides insight into the possibility of the robust navigation of unmanned aerial systems with complimentary sensors. The constantly increasing computing power allows the evaluation of big amounts of data and the development of new concept to fuse the information. Future navigation systems will use the data of all available sensors to achieve the best navigation solution at any time

Guidance, Navigation and Control for UAV Close Formation Flight and Airborne Docking

Unmanned aerial vehicle (UAV) capability is currently limited by the amount of energy that can be stored onboard or the small amount that can be gathered from the environment. This has historically lead to large, expensive vehicles with considerable fuel capacity. Airborne docking, for aerial refueling, is a viable solution that has been proven through decades of implementation with manned aircraft, but had not been successfully tested or demonstrated with UAVs. The prohibitive challenge is the highly accurate and reliable relative positioning performance that is required to dock with a small target, in the air, amidst external disturbances. GNSS-based navigation systems are well suited for reliable absolute positioning, but fall short for accurate relative positioning. Direct, relative sensor measurements are precise, but can be unreliable in dynamic environments. This work proposes an experimentally verified guidance, navigation and control solution that enables a UAV to autonomously rendezvous and dock with a drogue that is being towed by another autonomous UAV. A nonlinear estimation framework uses precise air-to-air visual observations to correct onboard sensor measurements and produce an accurate relative state estimate. The state of the drogue is estimated using known geometric and inertial characteristics and air-to-air observations. Setpoint augmentation algorithms compensate for leader turn dynamics during formation flight, and drogue physical constraints during docking. Vision-aided close formation flight has been demonstrated over extended periods; as close as 4 m; in wind speeds in excess of 25 km/h; and at altitudes as low as 15 m. Docking flight tests achieved numerous airborne connections over multiple flights, including five successful docking manoeuvres in seven minutes of a single flight. To the best of our knowledge, these are the closest formation flights performed outdoors and the first UAV airborne docking