Computationally-Efficient Visual-Inertial Simultaneous Localization and Mapping for Spaceflight Navigation

This thesis represents an investigation into the application to spaceflight of the estimation techniques developed to solve the well-known robotics problem of Simultaneous Localization and Mapping (SLAM). This subject has been thoroughly studied in the context of ground and aerial robotics but its study for use in the spaceflight domain, where the dynamical, measurement, and computational challenges are often very different than in terrestrial applications, is less common. Further, the wide body of extant robotics research into SLAM can be difficult to approach and understand for space navigators with a more classical estimation background because of the differences in terminology and assumptions that roboticists have utilized over time. This work offers an overview of the development of SLAM in robotics and how it has been applied in that field, as well as an accessible approach to the problem for researchers with an aerospace background. This also leads into the development of a novel visual-inertial (VI) SLAM algorithm designed to achieve constant-time exploration and mapping while still integrating the full nonlinear dynamics of the space environment, handling the high update rates of inertial measurement units (IMUs), and incorporating the measurement information produced by a camera sensor. This algorithm is applied to 2D and 3D simulated and real datasets to demonstrate its capability to quickly generate accurate state estimates of both spacecraft and environmental variables.</p

Autonomous Navigation of Distributed Spacecraft using Graph-based SLAM for Proximity Operations in Small Celestial Bodies

Establishment of a sustainable human presence beyond the cislunar space is a major milestone for mankind. Small celestial bodies (SCBs) like asteroids are known to contain valuable natural resources necessary for the development of space assets essential to the accomplishment of this goal. Consequently, future robotic spacecraft missions to SCBs are envisioned with the objective of commercial in-situ resource utilization (ISRU). In mission design, there is also an increasing interest in the utilization of the distributed spacecraft, to benefit from specialization and redundancy. The ability of distributed spacecraft to navigate autonomously in the proximity of a SCB is indispensable for the successful realization of ISRU mission objectives. Quasi-autonomous methods currently used for proximity navigation require extensive ground support for mapping and model development, which can be an impediment for large scale multi-spacecraft ISRU missions in the future. It is prudent to leverage the advances in terrestrial robotic navigation to investigate the development of novel methods for autonomous navigation of spacecraft. The primary objective of the work presented in this thesis is to evaluate the feasibility and investigate the development of methods based on graph-based simultaneous localization and mapping (SLAM), a popular algorithm used in terrestrial autonomous navigation, for the autonomous navigation of distributed spacecraft in the proximity of SCBs. To this end, recent research in graph-based SLAM is extensively studied to identify strategies used to enable multi-agent navigation. The spacecraft navigation requirement is formulated as a graph-based SLAM problem using metric GraphSLAM or topometric graph-based SLAM. Techniques developed based on the identified strategies namely, map merging, inter-spacecraft measurements and relative localization are then applied to this formulation to enable distributed spacecraft navigation. In each case, navigation is formulated in terms of its application to a proximity operation scenario that best suits the multi-agent navigation technique. Several challenges related to the application of graph-based SLAM for spacecraft navigation, such as computational cost and illumination variation are also identified and addressed in the development of these methods. Experiments are performed using simulated models of asteroids and spacecraft dynamics, comparing the estimated states of the spacecraft and landmarks to the assumed true states. The results from the experiments indicate a consistent and robust state determination process, suggesting the suitability of the application of multi-agent navigation techniques to graph-based SLAM for enabling the autonomous navigation of distributed spacecraft near SCBs

Spacecraft/Rover Hybrids for the Exploration of Small Solar System Bodies

This study investigated a mission architecture that allows the systematic and affordable in-situ exploration of small solar system bodies, such as asteroids, comets, and Martian moons (Figure 1). The architecture relies on the novel concept of spacecraft/rover hybrids,which are surface mobility platforms capable of achieving large surface coverage (by attitude controlled hops, akin to spacecraft flight), fine mobility (by tumbling), and coarse instrument pointing (by changing orientation relative to the ground) in the low-gravity environments(micro-g to milli-g) of small bodies. The actuation of the hybrids relies on spinning three internal flywheels. Using a combination of torques, the three flywheel motors can produce a reaction torque in any orientation without additional moving parts. This mobility concept allows all subsystems to be packaged in one sealed enclosure and enables the platforms to be minimalistic. The hybrids would be deployed from a mother spacecraft, which would act as a communication relay to Earth and would aid the in-situ assets with tasks such as localization and navigation (Figure 1). The hybrids are expected to be more capable and affordable than wheeled or legged rovers, due to their multiple modes of mobility (both hopping and tumbling), and have simpler environmental sealing and thermal management (since all components are sealed in one enclosure, assuming non-deployable science instruments). In summary, this NIAC Phase II study has significantly increased the TRL (Technology Readiness Level) of the mobility and autonomy subsystems of spacecraft/rover hybrids, and characterized system engineering aspects in the context of a reference mission to Phobos. Future studies should focus on improving the robustness of the autonomy module and further refine system engineering aspects, in view of opportunities for technology infusion

Multihop Rendezvous Algorithm for Frequency Hopping Cognitive Radio Networks

Cognitive radios allow the possibility of increasing utilization of the wireless spectrum, but because of their dynamic access nature require new techniques for establishing and joining networks, these are known as rendezvous. Existing rendezvous algorithms assume that rendezvous can be completed in a single round or hop of time. However, cognitive radio networks utilizing frequency hopping that is too fast for synchronization packets to be exchanged in a single hop require a rendezvous algorithm that supports multiple hop rendezvous. We propose the Multiple Hop (MH) rendezvous algorithm based on a pre-shared sequence of random numbers, bounded timing differences, and similar channel lists to successfully match a percentage of hops. It is tested in simulation against other well known rendezvous algorithms and implemented in GNU Radio for the HackRF One. We found from the results of our simulation testing that at 100 hops per second the MH algorithm is faster than other tested algorithms at 50 or more channels with timing ±50 milliseconds, at 250 or more channels with timing ±500 milliseconds, and at 2000 channels with timing ±5000 milliseconds. In an asymmetric environment with 100 hops per second, a 500 millisecond timing difference, and 1000 channels the MH algorithm was faster than other tested algorithms as long as the channel overlap was 35% or higher for a 50% required packet success to complete rendezvous. We recommend the Multihop algorithm for use cases with a fast frequency hop rate and a slow data transmission rate requiring multiple hops to rendezvous or use cases where the channel count equals or exceeds 250 channels, as long as timing data is available and all of the radios to be connected to the network can be pre-loaded with a shared seed

VSLAM and Navigation System of Unmanned Ground Vehicle Based on RGB-D Camera

In this thesis, ROS (Robot Operating System) is used as the software platform and a simple unmanned ground vehicle that is designed and constructed by myself is used as the hardware platform. The most critical issues in the navigation technology of unmanned ground vehicles in unknown environments -SLAM (Simultaneous Localization and Mapping) and autonomous navigation technology are studied. Through the analysis of the principle and structure of visual SLAM, a visual simultaneous localization and mapping algorithm is build. Moreover, accelerate the visual SLAM algorithm through hardware replacement and software algorithm optimization. RealSense D435 is used as the camera of the VSLAM sensor. The algorithm extracts the features from the data of depth camera and calculates the odometry information of the unmanned vehicle through the features matching of the adjacent image. Then update the vehicle’s location and map data using the odometry information. Under the condition that the visual SLAM algorithm works normally, this thesis also uses the 3D map generated to derive the real-time 2D projection map. So as to apply it to the navigation algorithm. Then this thesis realize autonomous navigation and avoids the obstacle function of unmanned vehicle by controlling the driving speed and direction of the vehicle through the navigation algorithm using the 2D projection map. Unmanned ground vehicle path planning is mainly two parts: local path planning and global path planning. Global path planning is mainly used to plan the optimal path to the destination. Local path planning is mainly used to control the speed and direction of the UGV. This thesis analyzes and compares Dijkstra’s algorithm and A* algorithm. Considering the compatible to ROS, Dijkstra’s algorithm is finally used as the global path-planning algorithm. DWA (Dynamic Window Approach) algorithm is used as Local path planning. Under the control of the Dijkstra’s algorithm and the DWA algorithm, unmanned ground vehicles can automatically plan the optimal path to the target point and avoid obstacles. This thesis also designed and constructed a simple unmanned ground vehicle as an experimental platform and design a simple control method basing on differential wheeled unmanned ground vehicle and finally realized the autonomous navigation of unmanned ground vehicles and the function of avoiding obstacles through visual SLAM algorithm and autonomous navigation algorithm. Finally, the main work and deficiencies of this thesis are summarized. And the prospects and difficulties of the research field of unmanned ground vehicles are presented

Visual-INS Using a Human Operator and Converted Measurements

A method human operated INS aiding is explored in which the pilot identifies and tracks a ground feature of unknown position over a short measurement epoch using an E/O sensor. One then refers to Visual-INS. In contrast to current research trends, a human operator is entrusted with visually tracking the ground feature. In addition, a less conventional measurement linearization technique is applied to generate “converted” measurements. A linear regression algorithm is then applied to the converted measurements providing an estimate of the INS horizontal velocity error and accelerometer biases. At the completion of the measurement epoch, the INS is corrected by subtracting out the estimated errors. Aiding the INS in this manner provides a significant improvement in the accuracy of the INS-provided aircraft navigation state estimates when compared to those of a free/unaided INS. A number of scenario are simulated including with and without a constrained flight path, with single vs. multiple ground feature tracking sessions, and with a navigation vs. tactical grade INS. Applications for this autonomous navigation approach include navigation in GPS denied environments and/or when RF emitting/receiving sensors are undesirable

Spacecraft/Rover Hybrids for the Exploration of Small Solar System Bodies

This study investigated a novel mission architecture for the systematic and affordable in-situ exploration of small Solar System bodies. Specifically, a mother spacecraft would deploy over the surface of a small body one, or several, spacecraft/rover hybrids, which are small, multi-faceted enclosed robots with internal actuation and external spikes. They would be capable of 1) long excursions (by hopping), 2) short traverses to specific locations (through a sequence of controlled tumbles), and 3) high-altitude, attitude-controlled ballistic flight (akin to spacecraft flight). Their control would rely on synergistic operations with the mother spacecraft (where most of hybrids' perception and localization functionalities would be hosted), which would make the platforms minimalistic and, in turn, the entire mission architecture affordable

Autocalibrating vision guided navigation of unmanned air vehicles via tactical monocular cameras in GPS denied environments

This thesis presents a novel robotic navigation strategy by using a conventional tactical monocular camera, proving the feasibility of using a monocular camera as the sole proximity sensing, object avoidance, mapping, and path-planning mechanism to fly and navigate small to medium scale unmanned rotary-wing aircraft in an autonomous manner. The range measurement strategy is scalable, self-calibrating, indoor-outdoor capable, and has been biologically inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals (particularly bats), designed to assume operations in previously unknown, GPS-denied environments. It proposes novel electronics, aircraft, aircraft systems, systems, and procedures and algorithms that come together to form airborne systems which measure absolute ranges from a monocular camera via passive photometry, mimicking that of a human-pilot like judgement. The research is intended to bridge the gap between practical GPS coverage and precision localization and mapping problem in a small aircraft. In the context of this study, several robotic platforms, airborne and ground alike, have been developed, some of which have been integrated in real-life field trials, for experimental validation. Albeit the emphasis on miniature robotic aircraft this research has been tested and found compatible with tactical vests and helmets, and it can be used to augment the reliability of many other types of proximity sensors