ADVANCEMENTS OF VISION BASED AUTONOMOUS PLANETARY LANDINGSYSTEMS AND PRELIMINARY TESTING BY MEANS OF A NEW DEDICATED

EXPERIMENTAL FACILITY

Marco Ciarambino, Paolo Lunghi, Luca Losi, and Michèle Lavagna

Politecnico di Milano, Aerospace Science and Technology Department, via La Masa, 34, 20156 Milano, Italy

ABSTRACT

This paper presents the design and testing activities of anew experimental facility currently under development atPolitecnico di Milano, at the premises of the AerospaceScience and Technology Department (DAER). The pur-pose of the facility is the testing of novel Guidance, Nav-igation, and Control algorithms for autonomous landingon planets and small bodies, especially for vision-basedrelative navigation and hazard detection and avoidance.The design, integration and testing activities of the facil-ity are presented, as well as the suite of GNC algorithms,currently under development at PoliMi-DAER, that aregoing to be tested. The ultimate goal is to create and bringup to TRL 5 a whole Autonomous Guidance, Navigationand Control chain for autonomous landings.

Key words: Planetary Landing; Hazard Detection &Avoidance; Vision-based navigation; Trajectory opti-mization; Experimental facility .

1. INTRODUCTION

In the next years, autonomous navigation and landing ca-pabilities are going to play a crucial role for the nextspace system generation. A renewed interest in spaceexploration had brought to the development of numer-ous missions in which spacecraft autonomy and com-plex landing maneuvers are involved. ESA is work-ing together with ROSCOSMOS on a cooperative pro-gramme for Moon, Mars, and Phobos exploration: theLuna-Resurs Lander mission (Luna 27) is planned for2020 and is focused on the exploration of the lunar southpole. Part of the European contribution is PILOT (Preciseand Intelligent Landing using Onboard Technologies), asubsystem devoted to the validation of pinpoint landingand Hazard Detection and Avoidance (HDA) technolo-gies [1]. The two agencies collaborate also in the Ex-oMars programme, devoted to the exploration of Mars:in October 2016, despite the failure encountered in thelanding phase, the lander Schiapparelli was able to collectprecious data, that will be exploited to refine the designof the next 2018 mission, which includes the release of arover on the Martian ground [2]. Also a Phobos samplereturn mission, called Phootprint, has been proposed to

be launched in 2024. The autonomous landing problemhas been studied by NASA since 2006 in the frame ofthe Autonomous Landing Hazard Avoidance Technology(ALHAT) programme [3]. Part of the developed tech-nologies were tested on the Morpheus lander demonstra-tor, and they will be integrated in future exploration mis-sions. Also CNSA has recently performed its first lunarlanding with a lander/rover system in the Chang’e 3 mis-sion, while ISRO is planning to put a lander carrying arover on the lunar surface by the early 2018 in the Chan-drayaan 2 mission.Major improvements in Guidance, Navigation and Con-trol are still required to obtain a fully autonomous systemwith HDA capabilities: vision-based systems are one ofthe most promising technologies to achieve the requiredlevel of precision and accuracy. Extensive test and vali-dation activities are required to ensure the necessary levelof robustness: affordable and repeatable datasets fromreal mission are scarcely available, often lacking the ad-ditional data necessary for the computation of a ground-truth solution to compare with the obtained results. Ar-tificially generated images can be a good substitute, butthe required level or realism implies the use of computa-tionally intensive, high fidelity rendering algorithms thatmake difficult closed-loop testing (and often impossiblereal-time hardware-in-the-loop). The use of analog facil-ities, capable to simulate a landing in a scaled environ-ment, can supply repeatable and controllable data. Thispaper presents the design and testing activities of a novelCNG chain for autonomous landing, together with thesetup of a new experimental facility currently under de-velopment at Politecnico di Milano, at the premises of theAerospace Science and Technology Department (DAER).

2. HAZARD DETECTION SYSTEM

The hazard detection system under development atPoliMi-DAER is based on Artificial Neural Networks(ANN). If properly trained, ANNs are capable to au-tonomously find underlying rules that correlate one inputspace to the correspondent output, without any previousknowledge of the actual link between the twos. The ca-pability to operate also in conditions not explicitly con-sidered during the projects phase makes this kind of sys-tem very attractive for HDA tasks. Information extractedfrom a single-channel image provided by a monocular

ANN

Indices Extraction Haz. Map Computation Target Ranking & Selection

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Figure 1: Hazard detection and target selection system architecture.

navigation camera are processed by a ANN to generate ahazard map of the landing area, processed by the site se-lection routine to compute the new target. Figure 1 showsa logical scheme of the algorithm. Following, the hazarddetection method is briefly summarized. A detailed dis-sertation is available in [4].

2.1. System Architecture

The retargeting process is divided in 4 different sub-phases:

Input and preprocessing. A single channel 8-bit1024×1024 px image is assumed as input. During thehazard detection maneuver the spacecraft is assumed in anear vertical attitude, small deviations from nadir point-ing are corrected by applying a perspective transforma-tion.

Image processing and input assembly. The input im-age is analyzed and different indexes are extracted. Thisprocess computes low level information from the imagein order to reduce the data space in which the networkshould detect the morphological features of the terrain.The same image processing techniques are computed atdifferent scales to allow the ANN to grasp relative dis-tances and depths [5]. Considered indexes extracted fromthe image are local mean µ and standard deviation σ, to-gether with higher order information such as image gradi-ent Grad (1st order) and Laplacian of Gaussian LoG (2ndorder). LoG combines a Gaussian filter with the Lapla-cian operator, and it is often used as an edge detector inimages. It is defined analytically as:

LoG(x, y) = − 1πσ4

[1− x

2 + y2

2σ2

]e−

x2+y2

2σ2 , (1)

where x, y are the image coordinates and σ the standarddeviation. Indexes can be computed efficiently with lin-ear filters, to reduce the computational burden. Also theSun elevation angle above the horizon is considered, al-lowing the network to understand features at different il-lumination conditions.A total of 13 indexes are fed into the network to computeeach hazard map pixel: µ, σ, grad, LoG for three differentimage scales, plus the value of Sun elevation angle.

Hazard map computation and Target selection. extractedindexes are processed by a cascade neural network [6],that computes a hazard map. Danger is estimated by a

hazard index whose value spans from 0 (completely safe),to 1 (completely unsafe). The map is exploited to discardunsafe targets and to select the best landing site. All thesites that do not respect minimum requirements on safetyor dimension (taking into account the lander footprint,margined with the expected dispersion at touchdown) areimmediately discarded. The remaining candidates areranked according to 3 criteria: minimum hazard index,maximum landing area, minimum distance from the nom-inal landing site (to maximize the probability to computea feasible landing trajectory). The weight of these param-eters can be tuned to maximize the performance.

2.2. Ground truth dataset

High resolution lunar DEMs (5-2 m/point) from LROC1have been used as the base to craft an artificial imagesdataset. Resolution has been increased up to 0.3 m/pointwith the addiction of random small craters, bouldersand fractal noise following real statistical distributions[7, 8, 4]. Images are then rendered with ray tracing tech-niques2 with realistic illumination conditions assuming apinhole camera with 60◦ angle of view. Ground truth iscomputed directly from DEM data: the local mean planeof the DEM is adopted to estimate the local slope an-gle, while the maximum and minimum deviations fromthe plane express the local roughness. Slopes and rough-ness maps are projected in camera coordinates, to matchthe navigation images. Then, the ground truth hazardmap is assembled assigning a hazard index 1 for pixelsin shadow, 0.33 to those pixels which fail the roughnessor the slope criteria, 0.66 to those which fail both. Theremaining pixels are classified as safe and set to 0. Fi-nally, the ground truth solution is downsampled throughGaussian pyramids to the final hazard map resolution.

2.3. Performances

The system was tested on a set of 8 images (not usedin the ANN training) of four lunar regions rendered attwo different Sun inclination, 15◦ and 80◦. A lander of 3meters of diameter in footprint and a navigation error of15 meters at 3σ has been considered. Terrain roughness0.5m and slopes over 15◦ are considered unsafe.

1Courtesy of NASA and Arizona State University. URL:http://wms.lroc.asu.edu/lroc/rdr_product_select, last visit on: June 1,2017.

2Persistence of Vision Raytracer (Version 3.7). Retrieved fromhttp://www.povray.org/download/

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Figure 2: Target landing site ranking and selection.

(a) Ground truth. (b) Computed.

Figure 3: Comparison between the computed hazard mapand the ground truth solution.

A comparison between the ground truth and the com-puted hazard map are shown in Fig. 3: terrain featuresare correctly interpreted by the network. While the rank-ing of the candidate landing sites is shown in In Fig. 2. Alanding site can be classified as True Positive (TP), mean-ing a correctly identified target, False Positive (FP), anunsafe site classified erroneously as safe, and the corre-sponding False Negative (FN), and True Negative (TN).Defining the Safety Ratio rS as the fraction of true pos-itives with respect to the total number of landing sitesfound:

rs =TP

TP + FP, (2)

and defining the Correctness Ratio rC as the fraction ofcorrectly identified sites (TP) with respect to the true safelanding site in the image:

rC =TP

TP + FN(3)

the probability to select an unsafe site is minimized maxi-mizing rS , whereas as rC increases, the available landingarea increases. The whole performance can be assessedwith a unique index J expressed as:

J = r5SR1/5C (4)

where the two exponents privilege landing sites safetyduring J maximization. A True Positive as Target Land-ing site: in the worst case, the first False Positive positionin the ranking is position 39, (average 695). The obtainedmean Safety Ratio is 0.9649, meaning that over 96% ofthe landing sites found are actually safe. The routinewas coded in C++ and run on a AMD A10-7700K APU,

with a 64 bit Ubuntu 14.04 GNU/Linux operative system.Computation time is well below 440ms, with indexes ex-traction stage almost 50% or the total runtime. Recentimprovements in space qualified hardware for computa-tionally intensive tasks, will make possible to massivelyexecute the heaviest tasks in parallel, with expected dra-matic improvements [9].

3. ADAPTIVE GUIDANCE

Once a safe landing site is selected, the system must com-pute a new feasible trajectory toward the new target. Afuel-optimum criterion is followed, in order to maximizethe attainable landing area in subsequent target updates,that may be required as soon as smaller terrain featuresbecome observable. Here the structure of the algorithmis summarized, and some numerical results are presented.For a detailed description, see [10, 11].

The expected time of flight of the landing phase in whichHDA tasks take place is in the order of 1min, and themass is supposed to significantly change during the ma-neuver. Distances, for both downrange and altitude, aresmall compared to the planet’s radius; thus, the assump-tion of a constant gravity field with flat ground is appro-priate. Aerodynamic forces are neglected: the effects ofthe possible presence of atmosphere (especially for lowdensities, as in the case of Mars) could be omitted due tothe relative low velocity (on the order of 100m s−1), andthe associated forces can be treated as disturbances. Thetranslational dynamics of the spacecraft are expressed ina ground reference system as:

ṙ = v v̇ =T

m+ g ṁ = − T

Ispg0(5)

where r = [x, y, z]T , x is the altitude, y is the downrangedirection and z is the cross-range; g is the constant ac-celeration of gravity vector of the planet, Isp the specificimpulse of the main engine, and g0 the standard gravityacceleration on Earth. The thrust net magnitude is indi-cated with T = ‖T‖.

The thrust vector acts as the control variable. The massequation is linked to the control acceleration by thethrust-to-mass ratio P:

P = T/m = v̇ − g (6)

Then, the mass equation in system (5) can be rewritten asa first order linear ordinary differential equation:

ṁ = − PIspg0

m (7)

where P = ‖P‖.

The states r0, v0 and m0 at the initial time t0 are sup-posed to be known. At the end of the maneuver, at timetf, the final states rf and vf are constrained. Then, the op-timal guidance problem is to find a control profile T(t) tobring the system from the initial to the target final states,

compatibly with all the constraints imposed by the actualsystem architecture. For sake of simplicity is consideredt0 = 0. The main thruster is assumed to be tightly con-nected to the spacecraft body. Then, the thrust vector de-pends only on the attitude of the spacecraft, expressed bythe vector of Euler’s angle e and on the thrust magnitudeT . Then, the initial acceleration is function only of theinitial thrust magnitude. Moreover, at the end of the ma-neuver, the lander is required to be aligned with the localvertical on the Target Landing Site. In case of flat surface,this condition reduces to impose null horizontal accelera-tion. A total of 17 boundary constraints are then availablefor position, velocity and acceleration components: 6 oninitial states, 3 on initial acceleration (function of initialthrust magnitude), 6 on target final states and 2 on thefinal acceleration due to the final attitude requirements

r(0) = r0 r(tf) = rfv(0) = v0 v(tf) = vf (8)

v̇(0) = f(T0) v̇(tf) = [free, 0, 0]T

The acceleration is expressed in a polynomial form ofminimum order needed to satisfy the boundary con-straints:

v̇(t) =

[v̇xv̇yv̇z

]=

v̇0x + c1xt+ c2xt2v̇0y + c1yt+ c2yt2 + c3yt3v̇0z + c1zt+ c2zt

2 + c3zt3

(9)By integrating the acceleration twice and applying theboundary conditions, the trajectory is parametrized interms of time-of-flight tf and initial thrust magnitudeT0, that are considered as optimization parameters. Thethrust-to-mass ratio can be obtained from Eq. (6) and thethrust profile is:

T = mP (10)

where the mass profile is obtained by solving Eq. (7).From the thrust vector a complete guidance profile, interms of Euler angles and thrust magnitude, is easily ob-tained.

In addiction to boundaries constraints, the system is sub-ject also to path and box constraints. The initial thrustmagnitude is bounded to the thrust actually available on-board, while the time-of-flight must lie between its lowerand upper limit:

0 < Tmin ≤ T0 ≤ Tmax (11)0 < tmin ≤ tf ≤ tmax (12)

The theoretical tmax is determined by the amount of fuelon board mfuel, whereas the adopted tmin correspondsto the time required by the lander to reach the groundwith maximum thrust pointing downward. The angularvelocity of the spacecraft is limited by the actual con-trol torques MCmax given by the Attitude Control System(ACS). Torques are approximated by the decoupled termdue to the angular acceleration, which is a sufficientlyaccurate approximation in case of low angular speed.

Exploiting this approximation leads to the following in-equality:

Imax‖ω̇(t)‖ ≤MCmax (13)

in which ω̇ is the derivative of the rotational velocity vec-tor, and Imax is the maximum moment of inertia at initialtime t0. The spacecraft is required to remain in a conepointed at the target and defined by the maximum slopeangle δmax. This constraint has has the dual purpose toassure that the the lander does not penetrate the ground,even in presence of bulky terrain features near the land-ing site, and to limit the angle of view on the target, ascould be required by HDA systems [12]. The constraintis expressed by the inequality:

r2y (t) + r2z (t) ≤ r2x (t) tan2(δmax) (14)

Finally, the final mass must be included between the ini-tial value and the spacecraft dry mass. Since the masstrend is strictly monotone by problem construction, theonly constraint with respect the minimum mass is veri-fied:

m(tf) ≤ mdry (15)

3.1. Optimization Problem and Numerical Results

The optimization problem takes the form:Find T0 and tf, in the domain defined by the inequalities(11), that minimize the fuel consumption computed by theEq. (7), subject to constraints (13), (14), and (15).The optimization could be solved with any non-linearprogramming (NLP) solver: the choice of this solverhas a huge impact over the final convergence propertiesand computational time. A dedicated optimization al-gorithm based on Taylor Differential Algebra (DA) wasdeveloped. Instead to be modeled as simple real num-bers, quantities are represented as their Taylor expansionaround a nominal point. In this way, DA variables carrymore information rather than their mere punctual values.The computation of the objective function as a DA vari-able includes its sensitivity to the variation of the opti-mization variables, leading to find the optimal solutionin a reduced number of iterations, using only simple al-gebraic computation between Taylor coefficients. A de-tailed discussion about the developed optimization algo-rithm is included in [11].A series of different Monte Carlo (MC) simulations re-garding a realistic case of lunar landing were carried outto estimate the system’s performances. The parametersconsidered in the initial dispersion include initial posi-tion, velocity, attitude, amount of fuel on board, specificimpulse, spacecraft moment of inertia, available thrustand gravity acceleration. The case here reported as ex-ample represents a large scale diversion maneuver froma nominal altitude of 2000m, in which a random largehorizontal diversion (600m 1σ) is ordered. Obtained tra-jectories are shown in Fig. 4a: in all the cases, the ordereddiversion was found feasible by the guidance algorithm.A MC simulation is exploited also to assess the algorithmperformances in terms of attainable landing area and fuelconsumption. A series of 1× 105 random diversions be-tween±4000m along both the horizontal axes is ordered

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Figure 4: Adaptive Guidance Monte Carlo Simulation(from Ref. [11]).

from the same nominal conditions of Fig. 4a. The at-tainable landing area can be obtained by correlating opti-mization results together with the coordinates of the tar-get, as shown by Fig. 4b, in which only the feasible points(satisfying all the constraints) are shown. The attainablearea is approximately circular, with a radius larger than2300m centered at the nominal landing site, a perfor-mance better than what is required for similar scenarios[13]. The same simulation was exploited also to obtainan estimation of the computation time. Running on aIntel R© CoreTM i7-2630QM CPU at 2GHz of frequency,the mean computation time is 25.23ms with a standarddeviation (STD) of 7.16ms, a performance compatiblewith on-board computation.

4. VISION-BASED NAVIGATION

The Navigation Algorithm in development at PoliMi-DAER relies on a single channel camera working in thevisible spectrum and is based on Visual Odometry and Si-multaneous Localization and Mapping (SLAM) [14, 15].Salient features are extracted and tracked from the incom-ing images, while a local sparse 3D map of the landingarea is built and used for navigation: relative position andattitude of the spacecraft are optimized with Bundle Ad-justment (BA). Here a summary of the structure of thesystem is presented, along with results obtained from atest campaign made on synthetic images from the Lu-nar dataset. The proposed architecture for the NavigationSystem is shown in Fig. 5.

Figure 5: Navigation System architecture.

Features detection. The Oriented FAST and RotatedBRIEF (ORB) [16] is adopted as features detector: fea-tures are extracted from the first frame to initialize thenavigation. The computational burden of ORB is verylight, with good invariance to viewpoint and scale, andresiliency to different light conditions. To improve the

features distribution over the frame, the image is seg-mented into 64 sectors, in which features are extractedindependently. An upper bound of 300 features is set tobe extracted to maintain a low computational cost.

Features tracking. ORB features extracted from the firstframe are tracked on subsequent images with the pyra-midal Lucas-Kanade algorithm [17]. This tool worksprojecting known features on subsequent frames search-ing for correspondence with the new ones in a boundedregion. The pyramidal approach makes the algorithmmore robust to track large motions, searching for featuresstarting from the highest level of an image pyramid. Astringent culling keypoint procedure is applied to rejectwrongly tracked features or ones tracked with low ac-curacy, with the objective to constantly retain a modestnumber of features but tracked with high precision. Incase of tracking failure or each time the number of fea-tures drops below a threshold, a new detection is triggeredand tracking restarted. Still tracked features are mergedwith the new ones to constantly keep tracking the highestnumber possible.

Motion estimation. Motion estimation relies on corre-spondences between 3D features of a map and 2D trackedfeatures. The map is built in parallel by the algorithm it-self, and it is initialized by the first 2 frames. 2D to 2Dcorrespondences are exploited to implement the 5-Pointalgorithm [18] and retrieve the essential matrix E of thefirst image pair. The motion is obtained up to a scale fac-tor applying Singular Value Decomposition (SVD) andcheirality check. Outliers are rejected by RANSAC it-erations. From the obtained motion 2D features are tri-angulated implementing the optimal method proposed in[19]. Reprojection error check is made at every step and astringent culling policy is applied to discard wrongly tri-angulated points along with correspondent features. FullBA is finally adopted to refine the map. Whenever thetracking fails or is re-triggered, a new map is triangulatedand merged with the existent. Once the 3D sparse mapis computed and features are tracked, the camera poseis retrieved by solving the "Perspective-n-Point problem"(PnP). EPnP algorithm [20], which gives a non-iterativesolution both for planar and non planar scenes. The tra-jectory is reconstructed except for an absolute scale fac-tor. The scene scale can be computed through data fusion,filtering measurements coming from other sensors (IMU,Laser Altimeter): a proper navigation filter is currentlyunder development.

Optimization. Features extracted and tracked in imagesare affected by noise: the epipolar constraint is never ex-actly satisfied, and uncertainties affect the triangulatedpoints, causing an increasing drift of the trajectory. Bun-dle Adjustment is implemented on the navigation algo-rithm to counteract these effects. Levenrbeg Marquadtalgorithm is exploited to solve the optimization problem,which is implemented in two ways:Full BA. At the end of map initialization a full BA run ismade. Optimizing both triangulated points location andestimated pose between the two frames used for initial-ization.

Motion only BA. At the end of each motion estimationstep, retrieved camera pose is optimized keeping fixed theobserved map points.

(a) Reference true Approach trajectory.

(b) Reconstructed map and trajectory of theApproach dataset with downward pointingcamera.

Figure 6: Ground truth and reconstructed approach tra-jectory of the Lunar dataset.

4.1. Tests on Lunar Dataset

The system was tested on sequences of synthetic imagessimulating Lunar landing trajectories. The method de-scribed in Section 2 has been adopted to generate the im-ages. Two landing trajectories were considered, MainBrake and Approach. Main Brake is a constant thrusthorizontal brake maneuver over the Moon Planck crater,while Approach is a variable thrust approach phase ma-neuver representing the last part of the landing. Hereafterresults are presented for the Approach trajectory withfixed downward pointing camera running at 1Hz. Fig. 6ashows the reference trajectory, while Fig. 6b shows thereconstructed one along with the sparse 3D map built.In these results, the absolute scale factor is recovered inpostprocessing.

Fig. 7 shows the trend of the reconstructed pose com-pared to the ground truth. Error is shown in terms ofnorm of the difference, with RMSE below 2m on eachof the reference axis along the whole trajectory. Re-sults obtained at this stage are quite satisfactory and showthe whole potential of the Vision-Based approach, errorsof the order of 2 meters are obtained on a really largescale trajectory with a difficult motion and a non-optimal

(a) Crossrange trajectory. (b) Downrange trajectory.

(c) Altitude trajectory. (d) Attitude quaternions.

Figure 7: Reconstructed trajectory in time comparedagainst ground truth and error module.

features distribution. Future development will focus onthe improvement of the current BA implementation alongwith data fusion and scale factor direct estimation.

5. EXPERIMENTAL FACILITY

To further increase the TRL of the aforementioned algo-rithms, an hardware-in-the-loop experimental facility isunder setup at PoliMi-DAER premises. Since the scarceavailability of complete real landing imagery datasets,vision-based algorithms development relies widely onsynthetic images. To validate such approach, experimentsare necessary. Moreover, the whole navigation systemperformance can be assessed only connecting the com-posing parts together, to verify mutual influences. Thefacility is composed by a robotic arm carrying a suiteof sensors to simulate lander dynamics, a 3D planetarymock-up, an illumination system and control and testcomputers. The setup is shown in Fig. 8a. The goal isto reproduce the landing maneuver over a scaled but real-istic environment. The system is designed to verify eitherhardware and software breadboards up to TRL 4, with thepossibility to update the system in the future to carry outalso real-time hardware-in-the-loop simulations to qual-ify GNC technologies up to TRL 5.

Planetary mock-up. The selection of a proper scale factoris needed to properly determine the facility manufactur-ing requirements. It is assumed that the hazard detectionstarts at a maximum altitude of 2000m. For reasons ofcost containment, the choice of the robotic arm was re-stricted to the use of an already available hardware, withan operative envelope of 1m. Then, a maximum scalefactor of 2000:1 has been considered. The target accuracyfor a navigation algorithm at touchdown is in the order of10m, which corresponds to 5mm in the scaled environ-ment. To have a resolution of the terrain at least one order

(a) The facility running the test. (b) Landing image taken by the landing camera.

Figure 8: Experimental facility running a preliminary functional test.

of magnitude greater than the landing accuracy, a resolu-tion of at least 0.5mm is required. The scale factor can beadapted to simulate closer range maneuvers with higherdetails, due to the fractal structure of the Moon surface.Urethane foam has been chosen as material for the lunardiorama due to its surface finish that yields the correctoptical properties and because of its great workability.The overall dimension of the diorama (2400× 2000mm)was selected to exploit the full envelope of the roboticarm and a maximum field of view of 60◦ for the landingcamera. The model is divided in 8 tiles, each measuring1200×500mm. The Digital Elevation Model (DTM) hasbeen selected from the GLD-100 NASA LROC dataset3,in order to have mixed terrain features, from plains torough slopes. The model was enriched with low scale de-tail, non visible due to the limited DEM resolution, by theaddiction of small craters, boulders and fractal noise [4].

Robotic Arm. A Mitsubishi PA10-7C robotic arm is avail-able at DAER. It features 7 DoF and it is capable to han-dle a 10 kg payload in a operative maximum spatial rangeof 1.03m. At its end effector, the robotic arm carries thesensor suite, that is made up by a navigation camera anda range sensor simulating a laser altimeter.

Illumination system. It must be able to guarantee the real-istic light environment of the planetary surface during thesimulation. In particular, for planets without atmospherelike the Moon, diffuse light must be avoided. It is com-posed by an array of LED spotlights with narrow beamangle, and a non reflective black structure to prevent ex-ternal sunlight and internal reflections to jeopardize thesimulation accuracy.

Diorama Calibration. The actual milled diorama is gen-erally different from the numerical model, due to imper-fections during the production process. The camera tra-jectory relative to the terrain must be reconstructed withhigh accuracy, at least one order of magnitude better than

3Courtesy of NASA and Arizona State University. URL:http://wms.lroc.asu.edu/lroc/rdr_product_select, last visit on: June 1,2017.

the navigation algorithms expected to be tested on the fa-cility. Being the desired accuracy in the development ofnavigation algorithms up to 10m (3σ, which correspondsto 5mm at the target scale factor 1:2000), the target accu-racy in diorama calibration is better than 0.5mm. Densematching method was selected to perform the shape re-construction: several photos from different angles aretaken for each tile; than, structure from motion algorithmare used to obtain the camera pose for each image. Fi-nally, dense cloud point models are obtained by triangu-lation of optical features between different frames. Thetiles models are eventually assembled by means of Itera-tive Closest Point (ICP) algorithms, generating the finalmodel for the whole diorama. The final accuracy is esti-mated by the features reprojection error, resulted well be-low the 0.5mm threshold. An example of reconstructedtile model is presented in Fig. 9.

5.1. Test Campaign

Currently, facility functional test are ongoing, and GNCtesting activities are going to start during the summer2017. GNC algorithms will be tested through a "bottom-up" procedure: first, single subsystems are checked fortheir standalone performance; then, they are progres-sively joined together to check if relative couplings de-grade the performances; eventually, software-in-the-loopsimulations tests to validate the whole system in flight-like conditions.

Figure 9: Tile digital reconstruction by means of structurefrom motion technique.

6. CONCLUSION

A suite of tools and algorithms for vision based naviga-tion for autonomous landings in development at PoliMi-DAER has been presented. An hazard detector based ona single camera and artificial neural networks and an effi-cient semi-analytical adaptive guidance algorithm are inan advanced state, while an efficient vision-based nav-igation system is under development. An experimentalfacility based on a robotic arm to simulate lander dynam-ics and a planetary mock up is being built for their val-idation and test. The aim is the development and vali-dation of an entire Adaptive GNC chain for autonomouslanding: the position of the target selected on the hazardmap is transformed in a 3D point in the physical world bythe mapping process. The lander states are then utilizedby the guidance algorithm to compute a new trajectoryand perform a divert maneuver. Furthermore, possiblefuture developments of the testing facility will includealso a system update to host real-time hardware-in-the-loop simulation, to test algorithms on flight representativehardware.

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