MARCO HISTÓRICO

The conceptual overview of the navigation system proposed by the candidate is illustrated in the following figure:

Figure 3.1: Overview of the Navigation System

It fuses the information from an inertial measurement unit, a camera, a star tracker and an altimeter. The inertial measurement unit provides high-rate angular rate and acceleration measurements of the vehicle. The inertial measurement unit is used during the entire mission. It allows the propagation of the vehicle states between the low-rate measurements provided by others sensors. The camera provides images of the lunar surface in real time. These images are processed to extract both absolute and relative optical measurements. The absolute optical measurements are critical to get an accurate estimate of the spacecraft position and to achieve pin-point landing accuracy. These measurements are used from the middle of the descent orbit until the camera image resolution becomes too high compared to that of the images used to build the on-board database. The relative optical measurements bring an accurate estimation of the velocity at low altitude. Good velocity knowledge is crucial to ensure a soft landing. It is preferable to start the relative navigation with a good altitude knowledge, otherwise the filter might diverge (the magnitude of the velocity is not observable from feature tracks without a good altitude knowledge). Consequently, it is used after the convergence of the estimator using the absolute optical measurements. The star tracker increases significantly the attitude estimation accuracy. It is used only when the sensor is oriented toward deep

Vision-Based State Estimator

Accelerometer and gyroscope measurements

Inertial Measurement Unit

(IMU)

Feature position measurements in image and planet

frame Image Processing Software Feature Georefe- renced Database Camera Surface imagery Features are matched in a database Spacecraft estimated states Previous Images Features are tracked trough a sequence of images

To the guidance and control software

Se

n

so

rs

Altimeter Altitude measurements

space and when the main engines are not used (thrusters induce vibrations that perturb its operation). Better attitude knowledge has a positive impact on the accuracy of the velocity and the position estimation brought by the optical measurements. It also improves the accuracy of the propagation of the translational states of the vehicle. The altimeter measures the altitude of the spacecraft with respect to the surface. It is enabled as soon as the vehicle altitude becomes compatible with its operating range. It increases the accuracy of the vehicle altitude knowledge required for proximity operations and soft landing. It also increases the observability of the vehicle velocity through the relative optical measurements. A more detailed analysis of the sensor enabling sequence and sensor characteristics is presented in Chapter 4.

Since the lunar surface is covered with well-shaped craters, the candidate proposes to use crater detection and matching techniques for absolute navigation. The detection algorithm uses image segmentation techniques. The matching of the detected craters with those in the database is done using a stochastic approach. The derivation of the proposed crater detection and matching algorithm is presented in Chapter 5. The proposed image processing software for relative navigation consists in tracking Harris corner using differential optical-flow estimation method. More details are given in Chapter 6.

The information provided by the sensors is fused using a vision-based estimator. The vision-based estimator is implemented using the EKF algorithm. This choice is based on an extensive study of the state-of-the-art estimation techniques. In Chapter 7, each estimation technique presented in the literature review is implemented and analysed using a range and bearing tracking system as an example. The absolute optical measurements are fused using the so-called tight and loose coupling approaches (discussed in Chapter 8). Three approaches are investigated to process the relative optical measurements: pseudo absolute measurements, feature position estimation and epipolar constraint. The star-tracker measurement update uses directly the measured attitude quaternion while the altimeter measurements are processed using a method based on the surface mean plane. The filter implements two sophisticated measurement delay-recovery methods: state augmentation and tightly integrated state back propagation. The complete derivation of the proposed vision-based state estimator is presented in Chapter 8. The attitude estimation is decoupled from the translational state estimation. The attitude filter uses only the measurements from the gyroscope and the star- tracker while the translational filter fuses the accelerometer, the optical and altimeter measurements. The estimated states of these filters are exchanged and considered as uncertain

parameters by the other. The translation filter is implemented following a decentralized architecture

i.e. that the translational states of the spacecraft are estimated in two separated filters. Information is exchanged between them in order to keep their estimation synchronized. One of the translational filters fuses the optical measurements while the second fuses the altimeter measurements. The attitude filter as well as both translational filters process statistically independent measurements sequentially. The implementation architectures of state estimators are described in more details in Chapter 9.

The candidate proposes to assess the performance of the proposed system using end-to-end, closed- loop and high fidelity simulations. Synthetic images of the lunar surface are generated by PANGU during the simulation. In order to increase the realism of the image, alterations are added according to the characteristics of the image sensor, of the camera lens and of the lunar environment. On top on that, the candidate proposes behavioural models of the camera and of the image processing to speed-up simulations (low simulation time is crucial for Monte Carlo simulations or navigation filter tunning). These stochastic models described mathematically the behavior of the functional algorithm. Since, all noise sources are controlled by the used, it can be very useful to assess the robustness of the algorithm with respect to a given parameters. Finally, the candidate proposes to validate the navigation system with hardware-in-the-loop experiments in a scale laboratory environment. A flight- camera is embarked on the six degree-of-freedom robot moving along a rail mimicking the motion of the vehicle. A camera is oriented toward a lunar surface mock-up and provides real-time image to the navigation system. More details about the validation of the proposed navigation system are given in Chapters 10 and 11.