LandingNav: a precision autonomous landing sensor for robotic platforms on planetary bodies

LandingNav: A precision autonomous landing sensor for robotic platforms on planetary bodies

Anup Katake*a, Christian Bruccoleri a, Puneet Singla b and John L. Junkins c

aResearch Scientist, StarVision Technologies Inc., 400 Harvey Mitchell Parkway S, Suite 400, College Station, TX 77845, USA

bAssistant Professor, Dept. of Mechanical & Aerospace Engineering, Univ. at Buffalo, NY, USA cDistinguished Professor, Dept. of Aerospace Engineering, Texas A&M University, College Station,

TX, USA

ABSTRACT

Increased interest in the exploration of extra terrestrial planetary bodies calls for an increase in the number of spacecraft landing on remote planetary surfaces. Currently, imaging and radar based surveys are used to determine regions of interest and a safe landing zone. The purpose of this paper is to introduce LandingNav, a sensor system solution for autonomous landing on planetary bodies that enables landing on unknown terrain. LandingNav is based on a novel multiple field of view imaging system that leverages the integration of different state of the art technologies for feature detection, tracking, and 3D dense stereo map creation. In this paper we present the test flight results of the LandingNav system prototype. Sources of errors due to hardware limitations and processing algorithms were identified and will be discussed. This paper also shows that addressing the issues identified during the post-flight test data analysis will reduce the error down to 1-2%, thus providing for a high precision 3D range map sensor system.

Keywords: autonomous planetary landing, stereo vision, terrain mapping, dense range map estimation, localization

1. INTRODUCTIONAutonomous spacecraft navigation systems are desired to reduce mission costs while enhancing existing systems and enabling new capabilities for precision landing and future deep space missions. For example – exploration of comets, asteroids and moons of outer planets will benefit tremendously from on-board systems that autonomously and accurately determine spacecraft position relative to a planetary body. Using current technology1, a landing site would be selected using radar, images and spectral information obtainable from Earth, and then doing a closer survey with a probe. None of these techniques, however, can be as effective as an autonomous system that is capable of providing real time information during the actual landing phase, enabling the guidance, navigation and control system to execute appropriate maneuvers for precision landing. Furthermore, mission operations or unforeseeable circumstances can lead to the need of landing in terrain that has not been previously explored or mapped. In this case, it is highly desirable to equip the lander with a sensor system that is capable of global and local positioning, obtaining range maps to the terrain during descent, evaluating the range maps to detect landing hazards such as rocks, boulders, crevasses and large slopes, and identifying a safe landing site.

The landing operation usually consists of three phases – de-orbit, braking burn and terminal descent. Each of these phases requires a different information. In the de-orbit phase, it is essential to determine the global position of the vehiclewith respect to the planetary body while during the braking burn period the spacecraft needs to be localized with respect to local frame of reference. The terminal descent phase requires a detailed knowledge of the terrain (3D range map) in order to detect landing hazards and identify the landing site. Given the distinct types of information required, a single sensor capable of meeting the mission requirements during all the three phases is extremely difficult to design. Thus, various methodologies and sensors targeting specific phases of the mission have been proposed. For obtaining global position information, landmark identification based methods such as crater identification2,3,4 have been used. Previous work5,6 for planetary navigation has explored the use of vision systems for motion and range estimation in the intermediate and terminal descent phase. The Autonomous Landing and Hazard Avoidance Technology community (ALHAT) led by NASA-JPL has been instrumental in developing, evaluating and integrating several sensor systems as well as developing methodologies and algorithms7,8. LIDAR based sensors9 are also being investigated and provide

* [email protected]; phone: 1 979 260-5015; fax 1 979 260-5042; starvisiontech.com

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several advantages such as illumination invariance, long range operation when compared to conventional imaging systems.

As a part of a funded effort from NASA, StarVision Technologies Inc. has been developing a precision autonomous landing sensor called LandingNav. The LandingNav system overcomes the range resolution problem of conventional stereo vision based systems by incorporating an innovative multiple camera configuration that not only results in achieving the mission requirements but also leads to more manageable imaging sensor sizes (4 Megapixels to 1Megapixels). The stereo imaging system, together with an image processing module, detects features and estimates their shape and relative position based on the range information. The visible features are then added to the local map and correlated with inertial navigation data to estimate the feature positions in the global map. Inertial attitude and position along with their rates are estimated from stereo vision based measurements by an on-board extended Kalman filter.

The organization of the this paper is as follows. We begin by a brief overview of the LandingNav system. This is followed by a description of the flight tests that were performed on a Cessna airplane in order to validate the multiple field of view system. A detailed discussion on the analysis of the acquired image data and results is then provided. Finally, the problems faced during the tests and possible improvements are discussed.

2. THE LANDINGNAV SYSTEM The limitations of the fixed baseline stereo vision with respect to range resolution are well known10 and thus motion stereo is often employed to increase the baseline. However, given the stringent requirements on the range errors (0.1m from 4m to 100m, 1m from 100m to 1km, and 10m from 1km to 5km range - driven by NASA requirements at time of the project), conventional motion stereo methods require a very large baseline and/or a very large imaging sensor (~10's of Megapixels). The LandingNav system is a multiple field-of-view(FOV) visible imaging system comprising of two banks of three cameras each. Each bank consists of a large FOV, medium FOV and a small FOV camera with each camera head performing low-level image processing such as exposure and gain control, image data packaging and transmission to a central processing station. The two banks are located at fixed separation on the spacecraft. During the braking burn phase, each of the banks tracks a footprint on the terrain. Motion stereo is performed on each FOV – the largest FOV provides a coarse range map that is refined when the smaller FOVs transition across the wide FOV footprint. The relative geometry between each image acquisition time-step and the interlock between each camera is estimated by using robust feature detection, tracking and geometry estimation methods.

Additionally, the angle between the boresight of each large FOV results in direction vectors to the features that are separated at substantial angles at large ranges thereby improving the triangulation and observation geometries. The tracked features along rate information from an on-board inertial measurement unit (IMU) are integrated into an extended Kalman filter (EKF) for spacecraft localization with respect to a terrain-fixed reference frame (TRF) and for the estimation of the co-ordinates of the tracked features with respect to the TRF. As the spacecraft travels over the terrain, the TRF will not be visible after a while and is thus continuously shifted based upon the newly detected features. During the descent phase, the large FOVs provide fixed baseline dense stereo maps that can be processed to detect hazards as defined by the mission requirements. To enable high speed dense stereo map computation a custom field programmable gate array (FPGA) processing platform was designed and developed. The processor is capable of estimating disparity maps of 2048x2048 pixel images using the Census transform method with a search range of 96pixels and a 7x7 window at rates of 5Hz.

3. CESSNA FLIGHT TESTS A set of 3 digital cameras, designed and developed at StarVision, were collocated in a mechanical assembly as shown in Figure 1. Although the LandingNav system consists of 6-cameras i.e. 2 sets of 3 cameras located on opposite sides of the body, only 1 set is sufficient to test the algorithms, validate integration and yield basic performance measures such as range data. The assembly was mounted on a wing strut of a Cessna 172 aircraft. A laser range finder was also mounted looking directly below to provide an independent measure of the range to the terrain. An inertial navigation unit, made by CloudCap, consisting of linear and angular accelerometers, GPS receiver, and altimeter was also used.

Images and data were acquired during a series of test flights at various altitudes ranging from 150m to 500m and over different types of terrain. Owing to the data transfer speed limitations the maximum frame rate at which the images could



be acquired was set to approximately 1Hz. Note however, that the cameras themselves are individually capable of 15Hz full frame i.e. 5Mpixels. Data from the CloudCap Piccolo IMU and the laser range finder was synchronized with the image capture master clock and logged continuously over the entire flight time.

Figure 1: The 3-camera system being assembled for flight tests

Figure 2: Camera installation on the Cessna 172 strut. A compact laser range finder, looking straight down, is also mounted on the aircraft window frame



4. FLIGHT TEST DATA ANALYSIS & RESULTS Since it was not possible to have all the software running on the embedded computer and it was not feasible to do hardware in the loop computations during the test flights, algorithm validation has been done by batch processing the data saved from the test flights. The objective of these tests was to:

Create dense terrain reconstruction using motion-stereo imaging. Measure the accuracy of the range estimation on real world images, from a significant height. Validate that using cameras with different level of zoom (i.e. different FOVs) allow for more accurate results in the range computation. Validate that it is possible to track the smaller FOVs within the large FOV notwithstanding the very different resolution. Validate that the localization problem could be solved by using visual features only, without a priori knowledge of terrain landmarks.

The large amount of data collected during the flights was converted and stored in a variety of convenient formats to improve ease of integration. The images were tagged with a unique identifier that allow us to correlate which camera took them and the approximate time (within few milliseconds) at which the images were acquired. With such tags, the images and the GPS or range finder data, also time stamped, were correlated to the images. Using a number of heterogeneous sensors without a common interface and different output rates has made the correlation of images and the GPS or range data more difficult, but the availability of independent measurements allowed us to compare the expected range to the terrain computed from the GPS position of the aircraft with that measured by the range stereo algorithms and the localization.

In order to obtain dense stereo range maps to the terrain, the following tasks were performed: Intrinsic calibration of the cameras Relative geometry estimation (motion stereo) between two views Rectification of the two views Disparity estimation Transformation to a range map

Intrinsic parameter calibration was performed on each of the three cameras using standard calibration methods and patterns. The estimated parameters included – focal length, principal point offsets and distortions. The re-projection error after the coarse calibration was on the order of 0.4pixels in the two directions. Although, an error around 0.1 pixels is desirable, given the constraints on the placement and mounting of cameras, additional calibration and fine tuning was not practical.

4.1. Relative Geometry Estimation An estimate of the relative geometry is required so as to perform rectification i.e. alignment of the two images. Feature detection and tracking using the KLT tracker was performed on the selected images. A maximum of 2000 features were detected and tracked across the consecutive frames. To obtain a robust geometry estimate, a random sampling method – MAPSAC was used to eliminate outliers that do not meet the geometry consensus. The standard MAPSAC algorithm was modified to reject feature selections that located very close to each other, as this was found to give rise to an even larger geometry errors leading to misalignments during rectification. A point-to-point distance based method was used to select the feature points so as to lead to a quasi-uniform distribution. This modification resulted in a significant reduction in the re-projection feature matching error – error decreased from around 0.38 to around 0.09 for the quasi-uniform points.



Figure 3: Detected and tracked features across a pair of frames

Figure 3 shows the features detected and tracked between a set of images – the blue vectors indicate the corresponding correlated feature in the next image. Figure 4 shows the tracked features using the modified MAPSAC algorithm. Note that as compared to the previous figure, the features indicated in the MAPSAC result are consistently oriented with very few outliers thereby proving the efficacy of the modified MAPSAC in rejecting outliers and erroneous tracks.

Figure 4: Modified MAPSAC tracked features - Day3 Capture18 Pair 06-07. Very few outliers are observed after the geometry estimation and re-projection.

4.2. Rectification Using the MAPSAC estimated relative geometry, the images were rectified to result in aligned images. Multiple methods – standard rectification and polar rectification were evaluated. The polar method, although is very robust with respect to the location of the epipole in the image (thereby indicating zoom motion), was not required for the given flight data since mostly lateral motion was performed. Figure 5 below show a small subset of the images that were rectified. The alignment error (i.e. the vertical offset between identical features) is small <1pixel over megapixel of image sizes.

4.3. Disparity Map Estimation The rectified images were then input to the disparity estimation engine to result in disparity maps to the terrain. The disparities were estimated using the illumination invariant Census algorithm that has been ported over to an embedded highly parallel implementation on a FPGA. The disparity search was varied from 16 pixels to 224 pixels (for large shifts). The disparity map was then post-processed eliminate occlusions by left-right consistency checks. Areas of low contrast were eliminated using the interest operator to result in a final disparity map that is accurate to a pixel level. Sub-pixel disparity interpolation was not performed on these images as the hardware logic had not been yet implemented on the FPGA processor at that time.



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From the above sets of images it was observed that valid disparity is obtained over regions of sufficient texture. Moreover, since the airplane was moving towards the left of the image, the disparity decreases from right to left in a continuous fashion. The obtained range estimates also are in qualitative agreement with the airplane motion – objects located in the right side of the image are closer than objects in the left side. To obtain a quantitative assessment of the range estimate accuracy, the laser range finder data corresponding to the image pairs was obtained. The range finder used was a single shot scanner-less system, thus it provided a measurement to only a particular point in the image. Altitude data from the GPS was used and the elevation of College Station , TX was factored in to obtain height above the ground surface. The results of the comparison are given below in Table 1.

Table 1: Comparison between true ranges and estimated range

Image pair Estimated baseline “True” Range Estimated Range Percent Error Day 2, 44-45 32.816m 221m 241.8m 9.4% Day2 , 52-53 49.682m 220m 236.3m 7.4% Day2, 49-50 39.171m 167m 176.8m 5.8% Day3, 05-06 31.16m 445m 540m 21.3% Day3, 44-46 31.42m 456m 325.5m 28.5%

We observed that except two sets of images (Day3), the percentage errors obtained are consistent with each other and are less than 10% at ranges varying from 167m to 256m. Note that the set where the least error occurs is also the set with the smallest range. This is to be expected since the theoretical error in stereo ranging increases with increasing range in a quadratic fashion. One of the primary innovation of the LandingNav system was created to address precisely this problem. In all the above sets, the images were obtained from the largest FOV camera (40deg), which is a standard stereo FOV configuration. To increase the relative disparity, we had proposed cameras with multiple FOVs while keeping the net number of pixels to be manageable. Thus it was expected that the medium and the small FOVs (each still being 2Mpixel and 1Mpixel resp.) will lead to better range estimates. The following set of data confirms our approach and validates the multiple FOV concept.



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Table 2 shows a quantitative comparison of the errors in the range estimates. As compared to the error obtained from the large FOV, the medium FOV error is about 5.3x smaller. This is a very promising result that confirms our multiple FOV design approach and makes us believe that it is indeed possible to obtain high accuracy stereo range maps that were previously not possible given a standard stereo vision approach.

4.5. Multiple Field-of-view Tracking In the proposed LandingNav configuration, the smaller FOVs are included within the larger one. They produce images with a higher resolution which is needed to improve the accuracy of the range estimation and of the localization filter. In order to merge the image features located by the tracking algorithm on the larger images with those located on the lower resolution images the first problem to be solved is that of tracking (i.e. locating) the smaller images within the bigger one. Given the fixed geometry and the known scale factors between the optics, the problem could easily be solved analytically and with calibration of the relative camera position. The objective of the system is however more ambitious: since it should be possible to match the location of the smaller FOVs within the bigger ones even at different instants of time, thus increasing the area for which high resolution range data is available.

Such problem would be trivially solved with a two dimensional correlation if the images were all of the same resolution, but this is not the case in this application. Therefore a robust matching algorithm must take into account the dramatic difference in scale between the different frames and possibly even rotations (or more generally affine transformations) between the different FOVs. The algorithm chosen for this task is called SURF, or Speeded-Up Robust Features. This algorithm is a variation of the golden standard for solving this type of problems which is currently SIFT. The choice of SURF is justified by the simpler implementation and by the availability in public literature of documentation and test code for SURF and by its excellent performance which equal if not outperform those of SIFT. For details on the SURF algorithms and its implementation the reader is directed to readily available literature11, in this section we present only the results of using the SURF for image tracking.

The SURF algorithm can be decomposed in two parts. In the first pass interesting parts of the images are marked, i.e. those parts of the images in which stronger gradients are present. In the second pass the algorithm computes scale and rotation invariant feature descriptors around the interest points in the image. A feature descriptor is an N dimensional vector, with N being 64 or 128 components according to the type of descriptor chosen. SURF features for an image from the low resolution camera are shown in Figure 11 and Figure 12: the size of the green boxes is proportional to the scale of the selected features. The choice of features at various scales is the main strength of the SURF algorithm. Once the descriptors have been computed on a pair of images, the match is done by computing the Euclidean distance between the descriptors on the first image with those of the second image.

The pair of descriptors that yield the minimum distance are candidate for a match. In order to reduce the number of false matches two other conditions are checked:

That the distance d0 between the best pair and d1 between the next best pair satisfy a relation of the type d1 > k d0 with k being a positive real number greater than 1. This ensure that there aren’t other pairs which are almost as good as the best one, thus the match is very likely to be unique. For the images shown in this report k = 1.2was empirically chosen to yield good results. That the match between the features is good in an absolute sense, i.e. that the distance between descriptors is smaller than a given threshold value, for this particular case such threshold value was chosen to be = 0.3.

The results obtained with one of the image set from the flight tests are shown in Figure 13 and Figure 14. The smaller FOV is found within the larger FOV; in these figures only the matching features are shown for simplicity but the algorithm is actually using a much larger number of features during the comparison.

The results of this validation test are satisfactory, in that both the smaller images can be found and tracked correctly within the bigger one, notwithstanding the big variations in scale between the images. The parameters of the algorithm must be better tuned, however, for a type of terrain that is closer to the intended application to lunar landing. The current parameters work well in an environment with strong contrasts, like those of roads and buildings with respect to the surrounding vegetation. A much more careful tuning would be necessary to allow a robust matching in a more uniform type of terrain.



Figure 11: Large FOV with SURF features highlighted as green boxes.

Figure 12: Image from smallest FOV with SURF features.

Figure 13: Image from the large 40º FOV with matching features from the smaller 5º FOV deg highlighted by the green boxes.

Figure 14: Small 5º FOV features correctly matched from the larger FOV in Figure 13.

4.6. Localization The localization module is responsible for estimating the position of the spacecraft with respect to a reference frame attached to the terrain. By incorporating the measurements from the IMU and a laser range finder (for initial guess), the spacecraft 6dof state can be estimated using the tracked features in successive images. Thus a nonlinear least squares (NLS) as well as an EKF was designed and developed. However, the data obtained from the IMU was not of sufficient



quality (breaks in the data) and additionally, the limited update rate of the image acquisition led to features being untrackable over a period of 3-4 frames due to high relative motion. Thus it was not possible to test the EKF over the acquired data. Simulations were carried out to prove the validity of the EKF approach. Results from the EKF and NLS (not shown here due to limited space) reveal that the NLS based approach is sensitive to the number of tracked features between consecutive frames (higher the number the better the performance). As compared to the NLS the EKF does not require a large number of features but rather that the features remain visible in multiple (~10) consecutive frames, and accurate correlation between the IMU data is also essential. Furthermore, to make the problem observable an initial estimate of the range (can be obtained from the NLS or the single shot laser range finder) is highly desirable. The optimal architecture for solving the localization problem is currently still being investigated.

5. DISCUSSION & IMPROVEMENTS The results presented in the previous section indicate an error of less than 10% at 500m range in range map estimation using the large and medium sized FOVs. The error in the estimates can be attributed to several factors some of which are:

Imaging Hardware o Image Acquisition Rates: The current image acquisition setup (3 cameras) is limited to 1Hz full frame

transfer due to data transfer and storage rate bottlenecks. A 1s update rate at the given altitude and speed results in about two-thirds of overalap between consecutive frames. This implies that the features detected in image number 1 are no longer visible in image 4. This leads to a bad condition for the EKF and localization wherein the origin needs to be constantly shifted and reattached to the terrain. This leads to adverse effects on the EKF convergence and stability, thereby affecting the estimate of the baseline between the frames.

o Exposure Settings: The current designs, incorporate simple auto exposure controls. However, it was noticed that this alone was not sufficient for correct exposures. Hence a more rigorous and autonomous exposure setting system is required to result in evenly exposed images over multiple time frames.

Rectification The rectification output is highly dependant on the validity of the relative geometry estimate and is very sensitive to the epi-pole location. In many cases, the rectified images were found to be misaligned by more than the Census mask size of (7x7) pixels, thus rendering them useless. A more robust rectification approach is thus required. Although the perfect rectification algorithm still remains an open problem for the computer vision community, multiple approaches can be taken to alleviate this concern. One such possible approach is extend the highly parallel FPGA implementation to allow searching in two dimensions instead of one, thereby getting rid of the rectification process altogether. In the current implementation, the misalignment in the best case is on an average 1pixel over the entire image. Thus the contribution to the error in the disparity image can be thought of as being equal to 1pixel. Image Smear In the multiple datasets obtained it was noticed that the images from the medium and the small FOV are smeared. This can be attributed directly to the image acquisition rate. Smearing leads to a loss in resolution and therefore a loss in reliable features. It was not possible to use the small FOV image data at all due to heavy smear. Thus it was not possible to analyze the highest resolution images. A remarkable improvement in the accuracy was noticed in using the images from the medium sized FOV. Following the same trend, it is expected that the using the small FOV images will result in higher accuracies in the range estimates. Disparity Estimation The current implementation does not provide a sub-pixel level disparity estimate and thus the estimated disparity can be in as much error as 0.5pixels. Although we have designed the algorithms for the sub-pixel disparity interpolation, hardware implementation and integration is yet to be performed. Doing that will thus result in a more accurate disparity estimate and consequently lower range errors. IMU Data obtained from the IMU was not continuous. Due to the interruptions the IMU was not reset, thereby leading to inconsistencies in the 6dof computations. Truth Data

o Differential GPS was not employed, thus there was a bigger uncertainty in obtaining the ground truth o A true model of the terrain was not available for cross-checking on a pixel-by-pixel basis. The range

finder provided only a single shot measurement to a very small area of the ground.



GPS derived Baseline Errors The baselines used for calculating the range maps were derived from the GPS data which itself is prone to error.

Adding up the possible contributions of each of the errors – rectification( 1pixel), smoothing due to blur and imaging residuals ( 1/7th pixel) and sub-pixel disparity (0.5pixel) results in an error in the disparity estimates close to 1.75 pixels. At a range of 220m this corresponds to a range estimate error of about 4m. The other factor heavily biasing the range estimates is baseline accuracy. An error of 1m in the baseline estimate (which is generous given the GPS accuracies) leads to an error of +- 8m. Thus the net error from just the disparity and baseline effects included is 12m or around 5.4%. Solving the above mentioned problems thus will lead to higher accuracy estimates and reduce the error to around 1-2% at the range values under consideration.

6. CONCLUSIONS In this study, the LandingNav multiple FOV system to enable autonomous landing on unknown terrain was designed, developed, fabricated and flown on a Cessna 172 airplane to obtain ground imagery for testing and validating the software modules. The primary software modules that were tested were the Stereo Ranging, Localization and Multiple- FOV Tracking. Multiple sets of images from the large FOV and the medium FOV camera were used to obtain relative geometries, disparity maps and range maps to the terrain, which were then compared against “true” values. Stereo ranging errors of less than 10% were obtained for ranges of 160m to 460m using non-ideal conditions. Given the right image acquisition setup and appropriate instruments such as IMU, it is expected that the error will be further reduced to less than 2%. A SIFT/SURF based FOV tracking algorithm was designed and tested using the obtained images. The algorithms were able to track the multiple FOVs flawlessly inspite of very limited features and in presence of large smear. The results of the series of tests done in this time frame all point to the successful testing and validation of the basic concepts and algorithms and provide a reason for optimism in the progression to a full-fledged sensor system.

ACKNOWLEDGEMENTS This work was funded by NASA SBIR Phase I, Phase II and Phase II extension grants from NASA – Johnson Space Center during the periods of 2006-2008.

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LandingNav: a precision autonomous landing sensor for robotic platforms on planetary bodies

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