Extraction of Common Regions of Interestfrom Orbital and Rover (Ground) acquired Imagery

Evangelos Boukas*, Antonios Gasteratos*, Gianfranco Visentin**

*School of Engineering, Democritus University of Thrace, Vas. Sophias 12, GR-67100 Xanthi,Greece. e-mail: evanbouk@pme.duth.gr, agaster@pme.duth.gr

**Automation and Robotics Section, ESAe-mail: Gianfranco.Visentin@esa.int

Abstract

Accurate global localization of space exploratoryrovers is of high importance to the future extraterrestrialmissions. In particular, the Mars Sample Return mission(MSR) needs precise localization techniques for the Sam-ple Fetching Rover (SFR) to collect a previously drilledcache of soil. Moreover, exact positioning is sine qua nonfor a successful rendezvous of the SFR with a Mars As-cent Vehicle (MAV) that will launch the load into Martianorbit. The utilization of prior information to refine the lo-calization has been the main approach in mobile roboticsduring the past decades. In extraterrestrial scenery, orbitalimages constitute an excellent source of such prior infor-mation. With long term aim to provide accurate globalrover localization, as a first step, the proposed paper as-sesses the ability to automatically detect common regionsof interest from both rover (ground) and orbital images.The approach comprises the separate extraction of salientregions, employing distinct techniques for the orbital andground images.

1 Introduction

1.1 MotivationOwing to the space exploratory roves being assigned

with ever more complex tasks, the increase in their au-tonomy is considered to be the next big step towards ad-vanced and cooperative extraterrestrial missions. The au-tonomy of space exploratory rovers is highly connected totheir ability to effectively localize themselves. In partic-ular, the Mars Sojourner rover (Pathfinder) beared auton-omy of 2-3 m per solar day (sol) employing only wheel en-coders and sun sensors. The MER rovers (Spirit and Op-portunity) have increased their autonomy by incorporat-ing visual odometry along with Inertial Measurement Unit(IMU) measurements. The most recent rover on Mars,namely the MSL rover Curiosity, has travelled more than5km (500+sols), however the visual odometry has beenused mostly for slip check, rather than for autonomousdriving. On the other hand, ExoMars rover will utilize

visual odometry for autonomous navigation as a nominalmode, thanks to a dedicated coprocessor [1].

An example of such an advanced mission is the forth-coming Mars Sample Return (MSR) campaign, which is ajoint effort of the European Space Agency (ESA) and theNational Aeronautics and Space Administration (NASA).The MSR has received international attention and its mainpurpose is to return 500g of Mars soil sample to Earth.The Martian soil will be, thereafter, analysed in Earth lab-oratories that include state of the art analysis instrumen-tation. The hardware and software of the rover that is re-quired to fetch the previously collected cache of Martiansoil has been targeted by ESA as another contribution tothe MSR campaign, namely the Sample Fetching Rover(SFR) [2].

1.2 Related WorkBearing in mind that an one-way signal to Mars re-

quires approximately up to 20 min, it is apparent that thenon autonomous navigation of rovers poses a noticeableoverhead to the exploration of the red planet. Therefore,recent and future space exploratory rovers are designedfor long traverses [3, 4]. This is the reason why the roversare equipped with several cameras. An example of cam-era setup design for such a specific application is reportedin [5], where a camera system for localization and map-ping of space exploratory rovers is proposed. The algo-rithms required for the long range autonomous navigationare 3D reconstruction, mapping [6], localization and pathplanning.

Moreover, notwithstanding the advanced and note-worthy mechanical and electrical design of space ex-ploratory rovers, their capabilities in terms of computa-tional and power needs are modest, especially comparedto contemporary robots. The algorithms for the localiza-tion, as well as for any other task, should be as lightweightas possible. Toward this end, the authors in [7] have im-plemented a Visual Odometry (VO) algorithm that is ca-pable of achieving state of the art results, while being oflow computational cost. In terms of power consumption,the authors in [8] have implemented the localization and

mapping algorithms on FPGA devices for ESA’s futurespace exploratory rovers.

In the context of orbital based localization, the au-thors in [9] employ descent imagery to create a DigitalElevation Map (DEM) of the area around the landing site.Nextly, they solve an image network utilizing bundle ad-justment based on manual selected tie points. The authorsin [10] propose a localization framework based on skylinematching. The skyline from the rovers imagery is firstlyextracted and then matched to possible skylines that havebeen extracted from a DEM. The matching reveals theposition of the rover. Li et al. [11] noted that with theaim to produce high quality localization estimates, orbitaland rover image networks should be connected through tiepoints. The work presented in [12] employed a Bayesianrecursive algorithm, namely a modified particle filter ableto retrieve the location of the rover on a global DEM. Theauthors in [13] proposed the employment of a LIght De-tection And Ranging (LIDAR) device in a system that isable to globally localize a rover laying within the imagingregion of a georeferenced DEM. Nevertheless, the inclu-sion of a such devices on Space Missions is not feasible, atleast based on current Space Agencies’ predictions. For amore detailed review of the state of the art on the localiza-tion of planetary exploration rovers with orbital imagingcan be found in [14].

Considering the aforementioned related works it isobvious that one of the most important features of a globalrover localization system is the acquisition of tie pointsamong the orbital and rover imagery. One of the mostcomplete works in the detection of salient regions for sci-entific analysis is the Autonomous Exploration for Gather-ing Increased Science (AEGIS) system [15]. The AEGISfocuses on the automated targeting of regions of intereston rover imagery. It includes an algorithm, namely theROCKSTER, for the identification of such regions. Thisalgorithm employs edge detection, flood fill and contourcalculation to cope with edge fragmentations. Howeverthe method described in this paper targets the regions thatare observable both on ground and aerial imagery.

1.3 ESA Network/Partnerning Initiative

As is the case with the aforementioned MSR mis-sion, it is clear that one of the most important abilitiesof future rovers, such as the Sample SFR, is the accurateglobal localization. Towards this end, the ESA fundedNetwork/Partnering Initiative entitled: “Methods to Re-fine the Self-Localization of Planetary Rovers Using Or-bital Imaging” aims to create a localization frameworkthat will employ orbital imagery. The rover is consideredto be “lost in space”, yet it is supposed to lay within anarea portrayed in an orbital image. Thus, the proposedmethod firstly assesses the extraction of regions of intereston the orbital images. In particular, it employees the mix-

ture of appropriate computer vision, morphology analysisand machine learning techniques to extract regions, whichare likely to be detected from the rover as well. Then, tak-ing advantage of the redundancy of the rover imagery, aprobabilistic approach is employed to extract regions thatare potentially detectable on the orbital images. From thispoint, landmarks are appropriate to be fed into a systemable to solve the “lost in space” problem. As the first stepto this system, this paper presents the methods employedin order to extract commonly observed regions of Interest(ROIs) from both orbital and rover imagery. The rest ofthe paper is organized as follows:

• The utilized dataset is presented in Sec. 2.

• The detection of Regions of Interest on orbital im-agery is presented in Sec. 3

• The detection of Regions of Interest on rover imageryis presented in Sec. 4

2 Dataset

Since our system is ought to be utilized in extrater-restrial scenery, the data upon which the model is de-signed and tested are of most importance. The datasetbeing utilized in the context of this research is the oneacquired along the framework of the SEEKER activity,which was carried out under the European Space Agency’sStarTiger initiative [16]. This dataset was collected in theChilean desert, Atacama after thorough analysis and hasbeen proven to be the most Mars like site on earth. Thedataset contains information acquired both by a rover andan orbital imagery analog.

2.1 Orbital Imagery (Analog)The orbital images have been produced employing

aerial imagery mounted on a Unmanned Aerial Vehicle(UAV). The UAV was utilized in order to produce or-thoimages with spatial resolution greater than 0.1m. How-ever, both the orthoimages and DEMs are downsampled tomost accurately reflect the real extraterestrial data that areavailable nowadays. The orthorectified and georeferencedorbital image that was considered in our setup and evalua-tion, along with the respective DEM is depicted in Fig. 1.The orbital images contain large rocks, both wide spreadand gathered. The rocks that appear on the orbital imageshave dimensions at least greater the resolution of the im-agery. This information is taken into consideration on thedetection of rocks on the rover imagery. Another type oflandmark that is clearly observable on the orbital imagesare the dry salt beds: salars. The salars are prominent,reflective and assemble the outcrops that are targeted andanalyzed by Mars rovers. These landmarks are as wellusable for a global localization system and, therefore, areincluded in our model.

(a) Orthorectified & GeoreferencedOrbital Image.

(b) Digital Elevation Model(DEM).

Figure 1. Complete Orbital Image and Dig-ital Elevation Model (DEM) of theSEEKER Atacama Dataset.

2.2 Rover Imagery

The rover employed in the dataset acquisition hasbeen built over the robovolc, which is a six wheel mo-bile robot capable to reach velocity up to 1m/s with max-imum operational time up to 7 hours. The SEEKER teamhas customized the robot in order to include, among oth-ers, the Bumblebee XB3 stereo camera , an Inertial Mea-surement Unit (IMU) and encoders for wheel odometry.The stereo cameras acquired images at a resolution of512px × 384px. Some indicative instances of the data ac-quired by the rover imagery are depicted in Fig. 2. The to-tal route of the rover is more than 5km long and can be seg-mented in three parts: The first segment is a 850m routecharacterized by direct sunlight that leads the path plan-

ning and localization to become quite challenging. Thesecond part is a 1.2km traverse that is distinguished by theexistence of large rocks, soft sand and noteworthy slopes.The final segment follows a 3km course with direct sun-light.However, the advanced difficulties of the dataset shouldbe noted, as well:

• The great instantaneous alteration in the roll axis, be-tween consecutive frames, pose an extra burden to-wards accurate local motion estimation.

• The direct sunlight augments the difficulties of imageprocessing algorithms, namely the Disparity estima-tion and the blob detection.

Moreover, the rover was equipped with a DifferentialGlobal Positioning System (DGPS). Unfortunately, dueto some specific DGPS system failures the measurementswere strangled and de-synchronized with rover image ac-quisition. However, via a filtering method and exten-sive processing we were able to result with a sparser thanintended but, nevertheless, highly accurate ground truth(Figure 1).

3 Detection on Orbital Imagery

Concerning the detection on orbital images, themethod employs the mixture of several techniques suchas a Hessian analysis for blob identification and an en-tropy based salient detector. The final step comprises aclassifier that distinguishes between the different types ofsalient regions (rocks, outcrops, etc.).

3.1 Multiscale Blob Detection3.1.1 Two-Sigma filter

The orbital images contain speckle noise due to nu-merous factors, such as the mosaicing procedures. There-fore, the first step of the multiscale blob detection com-prises the spatial filtering in order to remove any falsepixel values that were introduced. The filter employed inthe noise removal is the two-sigma algorithm [17] whichis know to suppress speckles while retaining edges. Thetwo-sigma filter is based on the assumption that the infor-mation follows a gaussian distribution with a mean valueµ and variance σ2 and therefore the 95.4% of the valueslies within the range [µ − 2σ, µ + 2σ]. If the number ofneighboor values lying within the range [µ − 2σ, µ + 2σ]is greater than a variable number K, the center cell valueis assigned to µ. In any other case the central case is as-signed with the average of the neighboring cells.

3.1.2 Hessian analysisThe Hessian analysis has been used for the detection

of prominent structures, including blob-like and ellipse-like shapes [18]. The computation of the Hessian matrix

(a)

(b) (c)

(d) (e)

Figure 2. Indicative instances of the rover’sroute. Special characteristics of thedataset: (a) Direct sunlight; (b) DenseRocks; (c) Salar regions; (d) Sparserocks; and, (e) Large rocks.

is performed employing the derivatives of Gaussian. Theprotruding and well defined shapes in the image, usually,are expressed as extreme eigenvalues of the Hessian ma-trix [18]. Therefore, we perform an analysis on the distri-bution of the eigenvalues where only the image points thatlay outside the 2σ around the mean of the distribution areconsidered as blob candidates.

The σ of the Gaussian can be considered as the mainparameter in the context of the scale space theory [19].The higher values of σ lead to the detection of larger re-gions. However, the scenes taken into consideration inthis work contain various types of blobs and, therefore, thedetection on multiple scales is required. At this point weshould note that the method introduces some false detec-tions which are, nevertheless, handled by means of classi-fication as described in section 3.2.

3.1.3 Local EntropyLocal entropy, which is based on Shannon entropy

[20], is a metric that represents quantitatively the random-

ness of the image intensity levels in an image (Eq. 1).The calculation employs the probabilities of each inten-sity to appear in an image neighborhood, similarly to thewhole image entropy. Therefore the probabilities insidethe neighborhood are calculated by firstly computing thehistogram of intensities and then by normalizing the his-togram to sum up to 1. The local entropy filter LE is pre-sented in the following equation:

LE = −

255∑i=0

p(i) · log2(p(i)), (1)

where i ∈ [0, . . . , 255] is the intensity and p(i) is the prob-ability of each intensity.

The blobs extracted with the aforementioned meth-ods are able to detect different and complementary re-gions. Therefore, a logical OR operation is performedon the detected regions to get the final blob image. Theresulting detection includes areas that contain holes and,therefore, we apply a binary image filing operation [21].The next step comprises a crucial parameter for the de-tection of commonly observable regions from both orbitaland ground imagery, i.e. the size of the blobs to be con-sidered. Depending on the orbital image resolution oneshould select the appropriate size. Since the best resolu-tion of state of the art orbital imagery on Mars (HiRISE) isable to capture images at a resolution of 0.25m/px, we de-fine the minimum size of blobs to be detected 0.5m×0.5m.Then the labeling of the the blobs is performed employinga connected component algorithm [22]. An example ofthe ROI detection is presented in figure 3. While the de-tection is able to extract most of the regions of interest,some false detections (mere sand) occur. The next sec-tion discusses the methods employed in the system thatenable, apart from the distinguishing between the types ofROI, i.e. rocks and outcrops, the discarding of these falsedetections.

Figure 3. An indicative example of the ROIdetection on a sample scene. It can beseen that the detection method is able todetect rocks as well as salars (outcropanalogous).

3.2 Classification for Sample Selection andAnnotation

This section describes the classification of previouslydetected ROIs on three classes: Rocks, Outcrops analogs(mostly salars) and Ground (mostly sand). Through theclassification procedure the annotation of the blobs is per-formed and any falsely detected regions, i.e. ground, areremoved.

3.2.1 Feature SpaceBased on the detected blobs, the feature space for

the classification can be defined. The features employedin our method are related to color, contrast and texture.Firstly, the actual intensity values of the blobs are con-sidered. The colorspaces that are taken into considerationare: Grayscale, RGB and HSV. Additionally, the local en-tropy has been employed to measure the randomness ofthe values in the ROI (sec. 3.1.3). Moreover, in order toinfuse the system with the ability to identify the variationsin texture, the Gabor features have been investigated. The2D Gabor filter is the modulation of a 2D Gaussian distri-bution with a sinusoidal with a specific orientation. TheGabor features are computed by the convolution of the in-put image with a number of Gabor filters with various:standard deviations of the 2D Gaussian (σ), orientationsof the sinusoidal and phase offsets (θ,φ).

3.2.2 Feature vector generationA feature vector of a ROI is defined as a N-

dimensional vector containing information about the blob.The structure containing the feature vectors of all sam-ples, i.e. ROIs, when concatenated in a matrix, is usu-ally referred to as data matrix and is the basis upon whichthe classification procedure is performed. The size of thedata matrix is M×N, with M being the number of ROIs.Due to different ROI sizes, in order to keep a fixed sizeof the data matrix, all blobs are resized to a specific size,i.e 20px × 20px independently the blob being larger orsmaller than this value.

The next step towards the feature generation is the di-mensionality reduction (DR) one, i.e. the projection ofthe Nraw dimensional sample to an N dimensional space,where N < Nraw, with as little loss as possible in infor-mation. The need for dimensionality reduction is twofold.On the first hand, there is the problem of computationalcost. One of the most commonly used methods for dimen-sionality reduction is the Principal Component Analysis(PCA) or KarhunenLove transformation which is an un-supervised method to produce a linear mapping of a highdimensional feature vector (and hence the data matrix) toa low dimension one [23]. An example of the dimension-ality reduction in the current system is the reduction of32800 dimensions (for all the types of features) to 119with a projection to the principal components that pre-serve 99% percent of the information.

3.2.3 k-NN classifier

The k nearest neighbors (k-NN) algorithm is a NNgeneralization where, instead of one, k neighbors are em-ployed [24]. The k-NN classifier has the following form:Given a set of N-dimensional labelled data and an unla-belled vector X of a new sample, the algorithm finds thek nearest neighbors based on a metric. The label of themajority of the nearest neighbors is assigned to the newsample. The only variable in the classifier is the k, whichis usually an odd number (for voting reasons) and may notbe a multiple of the number of classes. Considering thefact that this system employs three classes the candidatevalues for k are: {1,5,7,11,13}. The selection of featuretypes and parameter k was performed utilizing a leave oneout validation approach. The selected feature vector is theone containing the mixture of {HSV,GABOR,ENTROPY}and a k = 1. Although the over all classification rate is notexcellent, in the context of this work the classification ofRocks and Outcrops are adequate. The fact that really lowfalse positive exist, makes the classification sufficient.

Based on the aforementioned evaluation of the clas-sification the system is able to detect and label regions ofinterest from aerial imagery efficiently. Moreover the pre-sented method is able to perform sample selection, i.e todiscard any falsely detected ROIs, in order to retain theoutput free of noise. The detection on orbital imagery wasperformed over most of the parts of the Atacama dataset.Some indicative examples of detection and annotation arepresented in figure 4.

Figure 4. Indicative examples of the ROIdetection and annotation on the Ata-cama dataset. The red color indicatesrocks and the green color indicates out-crops

4 Detection on Rover Imagery

In the context of rover localization the most impor-tant data are those stemming from its own sensory, such asstereo cameras, inertial measurement unit (IMU), sun sen-sor, etc. In this work the detected ROIs from rover images,in contrast to previous works, have the main constraint andrequirement to be observable by orbital imagery as well.

4.1 Single Instance Detection4.1.1 Hessian analysis & Entropy

The first step on the detection on the rover’s imagesis similar to the ones presented in sections 3.1.2 and 3.1.3for the orbital imaging with small alterations due to thechange of perspective and level of detail. The hessiananalysis which is based on the extreme values of the eigen-values of the image. The main difference between this im-plementation and the one presented in the previous sectionis the range of the variable σ, which is the main parame-ter in the scale space theory and, contains hereby highervalues. The entropy has also been employed in this partof the system with small modifications, i.e the size of theneighborhood has been extended in order to detect largerblobs.

4.1.2 SaliencyIn theory an intelligent operator driving a vehicle

(even on the ground of Mars) would use visual landmarksto specify its relative position and route. This is the inspi-ration that led to the enhancement of the system with theability to detect visually salient regions. The saliency mapmodels are focused on replicating the human attentionalbehavior on a scene, i.e. the region that a human wouldfixate in a specific scene. There are several approachesthat have been proposed, the most important of which are[25]. The method employed on this work is the “Graph-Based visual saliency”[26].

4.1.3 3D Reconstruction for Rock ExtractionThe 3D reconstruction of a scene in front of the rover

utilizing a stereo camera is the process of assigning eachpixel in the stereo image to a 3D point in the world. Thealgorithm employs the computation of a disparity map,which represents the difference of the viewpoint on theleft and right image of the stereo rig and is presented in[27]. The simplified equation of the 3D reconstruction(neglecting lens distortions) can be expressed as follows:

[x, y, z] =

[xc · z

f,

yc · zf,

b · fdisp(xc, yc)

], (2)

where [xc, yc] are the coordinates of a point in the im-age plane and [x, y, z] are the 3D coordinates of the samepoint after the reconstruction. In order to extract the rocks

that protrude from the ground the V-disparity has been uti-lized. The V-disparity is a line-wise histogram of the dis-parity of a stereo image [28]. The V-disparity of a stereopair that contains mostly ground points, has the propertyto present the pixels of the image that depict the ground,on a line. Localizing the points in the V-disparity that are“above” the slope, one can detect the pixels in the imagethat correspond to rocks. An example of the detection ofrocks utilising the V-disparity is shown in figure 5.

(a) The V-disparity.

(b) The detected rocks over-plotted on the originalimage.

Figure 5. The detection of ROIs in a roverscene employing Entropy and Hessiananalysis.

4.2 Classification

Following the detection of the regions of interest on astill rover image the next step comprises their classifica-tion in one of the three types: rock, outcrop, ground. Thefeature space of the the blobs cannot be the same as theone presented in section 3.2.1 due to rover cameras. Thepossible feature vectors are formed utilizing the follow-ing types of information: Gray, Gabor, Entropy. PrincipalComponent Analysis is performed on the feature vectorfor dimensionality reduction and the resulting dimensionsof the specific feature vector are 98. The classifier utilizedin the system is the k-NN with the variable k set to 5. Theevaluation was performed utilizing a leave one out crosscorrelation approach. The final feature vector utilized isthe: {Gray, Gabor, Entropy}

4.3 Sequential Detection on Rover’s Route

Although the detection and annotation of the ROIs hasbeen presented adequately in this paper, there is still a veryimportant factor that has not been discussed yet. That isthe fact that the rover is not still, but is continuously mov-ing. Therefore, the detection on the rover should not beperformed on a single instance but rather on a sequenceof images. The basis of the detection on the sequence ofimages is the motion estimation (sec. 4.3.1).

4.3.1 Visual OdometryThe calculation of a rover’s route based on informa-

tion stemming from its cameras is referred to as visualodometry. The authors in [7] proposed a computationalefficient, yet effective algorithm for the computation of arovers motion estimation, based on a novel outlier detec-tion filter. This method has been employed as well in thissystem with the addition of an algorithm for the optimalfitting of consecutive point clouds, namely the IterativeClosest Points (ICP) [29]. Assuming a point cloud tP thatwas created through 3D reconstruction, as explained insection 4.1.3, the same set of point cloud is observableon the time t + 1, i.e. t+1P. Given the transformation be-tween the two consecutive frames the point cloud t+1P canbe projected relatively to the robot’s initial frame. How-ever, due to erroneous calculation of the transformation,the point clouds are never perfectly aligned and, thus, therover estimation may be subject to drifts. The ICP algo-rithm can provide a near-optimal solution to this problemby calculating the transformation required to accuratelymatch the two misaligned point clouds.

In the context of the global rover localization the de-tection of ground stemming ROIs on an fixed frame, in thesame way the orbital images are referenced to a fixed geo-graphical point. By incrementally computing incrementalmotion estimations the system can acquire the rover’s pose(position and orientation) relatively to its initial frame.

5 Conclusions

As space exploratory missions advance the need ofglobal rover localization arises. This need is inextrica-bly connected with the detection of commonly observedregions of interest on both orbital and ground imagery.The paper in hand presented the design, implementationand validation of a system that serves the aforementionednecessity. The system takes into consideration appear-ance features as well as geometrical characteristics of therover’s surrounding. The extraction of regions on the or-bital images is performed based on a mixture of entropyand hessian analysis. The detected objects are assignedwith a position relative to the rover’s initial frame, to-wards the global localization end. Our analysis indicatesthat the classification on both types of images is perform-ing adequately while, on the same time, it ensures that nofalse detection is inserted into our model. The proposedmethodology enables the detection of commonly observ-able regions from rover and orbital imagery.

5.1 AcknowledgmentThe proposed work is funded by the European Space

Agency via the Network/Partnering Initiative under thecontract No. 4000109064/13/NL/PA.

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