Obstacle detection in images

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Information about Obstacle detection in images

Published on March 18, 2014

Author: hasangamethmal

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Obstacle Detection in Images T.H .H Methmal (Reg No. 2010/CS/181, Index No. 10001816) University of Colombo School of Computing hasangamethmal@yahoo.com, 0711361409 A Literature Survey Presented to University of Colombo School of Computing in partial fulllment of the requirements for the Degree of Bachelor in Computer Science SCS3013 Literature Survey Courses Adviser: Prof. N. D. Kodikara Used harvard referencing style 4300 of word count Lyx tool used for format December 11, 2013 Copyright ©2013 by Hasanga Methmal. All rights reserved.

Abstract In this survey, we research about ways of obstacle detection technics in detail. Study will go through the importance of obstacle detection, various applications which are included obstacle detection kits and various technics to achieve obstacle detection. Mainly survey will discuss about three main areas of obstacle detection. Applications which are innovate to assist visually impaired people, Path planning using obstacle avoidance of mobile robots and various kinds of autonomous vehicles has built in obstacle detection system. As well as survey will review main obstacle detection technics such as monocular technics, stereo technics, depth maps generating technics etc. In the discussion all the technics will be compared and consider their pros and cons briey. Later part of the survey discusses about future enhancements and challenges in obstacle detection. i

Acknowledgements I would like to thank my advisor, Prof. N. D. Kodikara , for his experienced guidance and constant support throughout the period . I also be obligated to thanks to my family members and friends, whose support, well wishes and encouragements to brought me here. ii

Contents Abstract i Acknowledgements ii List of Figures v 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Background 2 2.1 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2.1 False Positive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2.2 False Negative . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2.3 Noise and Non-Linearities . . . . . . . . . . . . . . . . . . . . . . 3 2.3 3D versus 2D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3 Approaches to obstacle detection 4 3.1 Obstacle detection using 3D grid and 2D grids. . . . . . . . . . . . . . . 4 3.1.1 Method introduction . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.1.2 Stereo Measurement . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 Obstacle detection by using sequence of lters . . . . . . . . . . . . . . . 7 3.2.1 Ground/Non-ground Segmentation. . . . . . . . . . . . . . . . . . 7 3.2.2 Background Subtraction. . . . . . . . . . . . . . . . . . . . . . . . 7 3.2.3 Size lter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2.4 Appearance-based Recognition Filter. . . . . . . . . . . . . . . . . 8 3.2.5 Shape-based Recognition Filter. . . . . . . . . . . . . . . . . . . . 8 3.3 0bject collision detection algorithm based on disparity images. . . . . . . 8 3.4 Haar-like features with cascade classier . . . . . . . . . . . . . . . . . . 9 3.5 Fast Human Detection for Indoor Mobile Robots Using Depth Images . . 10 3.5.1 Depth image segmentation. . . . . . . . . . . . . . . . . . . . . . 10 3.5.2 Region Filtering and Merging . . . . . . . . . . . . . . . . . . . . 11 3.5.3 Candidate Classication . . . . . . . . . . . . . . . . . . . . . . . 11 3.6 Obstacle detection using Equivalent points. . . . . . . . . . . . . . . . . 11 3.7 Multi-cue Visual Obstacle Detection. . . . . . . . . . . . . . . . . . . . . 12 3.7.1 Geometric obstacle detection . . . . . . . . . . . . . . . . . . . . . 12 3.7.2 Color base obstacle detection . . . . . . . . . . . . . . . . . . . . 12 iii

4 Discussion 13 5 Conclusion 14 6 Future works 15 References 16 iv

List of Figures 3.1 Gate, obstacle, oor and bump regions that robots can move through. . . 4 3.2 Flowchart of the motion planning method . . . . . . . . . . . . . . . . . 5 3.3 Robot can see obstacles . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.4 Takes all measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.5 3D grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.6 C-Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.7 Implemented series of lters, . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.8 Depth image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.9 Algorithm owchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.10 Identifying the corresponding regions of distinct object in depth images . 10 3.11 Image after omit unnecessary lines . . . . . . . . . . . . . . . . . . . . . 11 3.12 False negative and false positive . . . . . . . . . . . . . . . . . . . . . . . 12 v

1 Introduction This survey refers how obstacle detection is done by various application areas. Also it is discussed about technics which are used in obstacle detection in images. Recently ob- stacle detection is a quit interesting topic among the researchers. Numerous applications are innovated dierent areas which are included obstacle detection kits. So survey will discussed mainly about three areas. Most of the vision systems of mobile robots are contain an obstacle detection system. Recently robots are hugely involving industrial production and military activities. Navigating through obstacle lled environments is a big challenge to mobile robots. So there are so many technics to apply to mobile robots to make easier the navigating task. As well as there are so many applications which are using obstacle detection system to assist visually impaired people. Those people suering from lack of information of their surroundings. So assisting applications detect various obstacles and analyze and present with details to the user. Now days there are lots of unmanned vehicles are produced for dierent kind of purpose. Such as military, farming and whether forecasting elds are using many aircrafts and vehicles which are come up with obstacle detection systems. 1.1 Motivation Vision base obstacle detection truly motivated by nature. Most animals gain dierent kinds of vision systems in nature. As humans we got eyes which providing amazing images. Eyes are giving us remarkable 3D information to us for obstacle detection. Flying insects have optimal ows which are giving them absurd navigation and obstacle detection capabilities. Actually obstacle detection system is an important sensor in robots and autonomous vehicle. Because it is a very crucial gadget to have when it in the unknown world. It may deliver very important information about its surrounding to succeed the navigation. So vision base obstacle detection directly commit to the safe operation of mobile robots and autonomous vehicle 1.2 Objectives Main aspiration of this survey is discuss about pros and cons existing algorithms and analyze their eciency. Research question of this literature survey is the most suitable real time technic is for navigating and path planning purposes to mobile robots. 1

2 Background 2.1 Applications Vision obstacle detection is a very interesting topic in recent past. Because there are so many applications have vision system these days. Mainly mobile robots have obstacle detection system[1] [2] [7]. When mobile robot enter to an unknown environment his vision system is very helpful to do their operations and survive in that environment. As well as obstacle avoidance is help to planning their path and reach to the destination[1]. Especially military services use these kinds of robots for dangerous operations[2]. As well as vision system use for create applications to help visually impaired people[3] [4] [7].They are suering from lack of information of their surroundings. Researchers have invented applications that can detect obstacles and give them a message where the obstacle is. Staircases are really challenging to go through for a visually impaired people. So there are applications for detecting staircase with details[4]. There are so many unmanned autonomous vehicles are using obstacle detection systems. When there is no one to control that vehicle has to have a good vision to navigate through the obstacles. In military services use unmanned aircrafts for spy enemy activities. Those helicopters have immediate landing emergency in case of dierent situations. Then helicopters have to nd minimum obstacle area to landing[10]. As well as there are applications that creates for farming purpose. There is an autonomous vehicle that has a vision system to obstacle avoidance. It can safely navigate through the fruit trees[11] . 2.2 Errors When we work with train data there can be some errors obviously. In vision obstacle detection has mainly two kinds of errors. 2.2.1 False Positive There can be an error that your obstacle detection system output has an obstacle. But In the real environment there is nothing. For Example if there is a paper on the oor system may be detecting it as an obstacle. But actually robot can easily walk on that paper. So it is false positive behavior. 2.2.2 False Negative There may be an error that truly environment has an obstacle. But your system not detects it. It is an false negative error. In those situations your devices can be harmed because of undetected obstacles. 2

2.2.3 Noise and Non-Linearities Noise is dened as some random dierences in brightness information of the image. Those noises are generated in lm grains or electronic noise in your input stereo camera. Re- searchers use dierent technics to eliminate the Noise and Non-Linearities. Random Gaussian noise and Kalman Filter are some of them. 2.3 3D versus 2D Many robots have 2D obstacle detection system. Solving 2D obstacle detection problem is much easier than solving 3D obstacle detection. 2D detection system always deals with intensity map at each pixel in the particular picture. It check whether obstacles to be identied and not identied. Very rst image indicates changes in the scene. But in 3D obstacle detection system have to picture the whole scene. 3

3 Approaches to obstacle detection 3.1 Obstacle detection using 3D grid and 2D grids. It is very important to know about its environment for beep robot while it is seeking path to reach a destination [1].This method will show how robot identies the obstacles and how plan its path through below mentioned obstacle regions.(Figure 1) Figure 3.1: Gate, obstacle, oor and bump regions that robots can move through. Specialty of this method is robot can move through above obstacle area and it can measure the obstacle. First robot capture images its surrounding and then generates a 3D grid using those images. After 3D grid is converted in to a 2D grid. 3.1.1 Method introduction There are some assumption in propose method. 1. All the objects in the environment divided into four classes such as oor, obsta- cle, bump and gate areas. So robot can recognize objects by shape and distance information. 2. Size and location of the landmarks antecedently. So robot knows its correct coor- dination. 3. Robot knows his destination in advance. So it plans the path to destination. Flowchart of the motion planning method is mention below.(Figure 2) 4

Figure 3.2: Flowchart of the motion planning method 3.1.2 Stereo Measurement Robot gets information about environment using a stereo camera. Figure 3 shows images of the environment. Then it will convert into the 3D model of the environment as shown in Figure 4 .Then robot can get all the measurements to build the 3D grid.(Figure 5) Figure 3.3: Robot can see obstacles Figure 3.4: Takes all measurements 5

Figure 3.5: 3D grid Method called conguration space (c  space) used for represent the robot. It considers the robot as a point without any size. As well as all the obstacles are call conguration obstacles(c-obstacle) in the environment and they are enlarged based on robot size. Figure 6 shows an example of c- space. Figure 3.6: C-Space After get all the 3D measurements it will create a 3D grid map as shown in gure 4.Then it converts to a 2D map.2D map can be classied into several region. Figure 6 shows all classied areas in 2D map. Those unmeasured area will be reduced by continues learning process. Now onwards 2D grid map recognize as a graph which is including nods and graph. Robot can plan its path by using graph traversing technics. 6

3.2 Obstacle detection by using sequence of lters This approach is a combination of stereo and monocular image-based vision algorithm[2] .Mainly this method use a sequence of lters through the color camera lters. First go through with the image Figure 7. Figure 3.7: Implemented series of lters, Then extract information of the foreground grouped into the blobs. Then those blobs will be ltered based on size model, appearance model and shape model. Several non- target blobs can be eliminated by this process and system can focus on possible targets. Most important thing is this method use series of lters and each model that is learn on the y from the previous model. 3.2.1 Ground/Non-ground Segmentation. Since this use for mine discovering robot we have to clearly identify the low- lying obstacles in the surface. For this purpose we use a correlation-based pyramidal stereo algorithm to get a threshold. We use the RANSAC algorithm to get an estimate ground surface. Give a series of 3D stereo points to algorithm and get an estimate value. According to that value we decide if it is a potential target or not. 3.2.2 Background Subtraction. Main distraction of the ground is moving objects. For overcome this problem algorithm create a composite image over the object moving time. Also create before and after the object is thrown. Analyze all the images and all the detected changes grouped into the blobs. 3.2.3 Size lter In this phase it lters according to the known size of the target object. Distance to the blob will be estimated using stereo. 7

3.2.4 Appearance-based Recognition Filter. We use only consider about the color. Because target objects are texture less. Robot doesn't' t know how is the target obstacle looks like in advance. So we have to present all sides of the object to the robot and train to recognize them. 3.2.5 Shape-based Recognition Filter. Most eective way to identify objects is its shape. Shape base recognition algorithm use to identify the object from the object classes. 3.3 0bject collision detection algorithm based on disparity im- ages. This technics mainly use for detecting obstacles with their depths[3]. This approach was used for an application which helps for the visually impaired people[1]. They really want detailed information about their surroundings. We use disparity image or depth map to storing the depth of each pixel in the image. Each pixel of the depth map corresponding to a pixel in the image. Near objects can be seen lighter color and far objects are darker.Figure 8 shows an example. Figure 3.8: Depth image Information about depth maps are stored using 64 gray levels. But there are more levels have to be added in dierent circumstances such as outdoor scenes or high quality pictures. Depth of a pixel is calculated by following formula. z = f × b d z is the depth, f is the focal lengths in pixels, B is the base line in meters and d is the disparity. Proposed algorithm is detected two levels of objects. One meter depth objects catch as a near closeness and two meter threshold for rst detection. Proposed algorithm briey 8

1. Breakdown disparity image with 2D Ensemble Empirical Mode Decomposition. 2. Output disparity images ltered to dispose of higher frequency and noise. 3. Dene two regions of interests (ROIs) to detect nearby objects in two levels. in this case we got one and two meters. Finally combine all the information of depth maps and apply into real image to excerpt real object information. 3.4 Haar-like features with cascade classier When a visually disable person walking through an area staircase is a major obstacle for him to go through[4]. So there are application which detect various staircases to assist them.[1] Main object of the technic is improving the accuracy of detection. To achieve this target we have to collect set of positive image set which are containing 4 to 5 steps and photos should be taken on various conditions. As well as we have to collect set of negative samples also which not contain any staircase. After that training is done until calcied into 18 layers. Note that training is performed in fewer steps than a normal application. Because always good to have a higher detection rate in staircase detection applications. When detection staircase most of time detect similar regions like staircase and they call candidate detection. For overcome this false detection, we use cascade classiers create from training session and the 40 * 40 sub windows with dierent sizes for scanning. Haar detector creates multiple hits in the staircase region and false detection has single isolated detection. Note that Stereo camera system produces very poor results for staircase detection although it gives very informative results with less false detection rate. That's why we have to come up with such algorithm.All the stages of the algorithm is shown in Figure 9. Figure 3.9: Algorithm owchart 9

3.5 Fast Human Detection for Indoor Mobile Robots Using Depth Images One of the biggest challenges for mobile robots is detect moving humans[5]. Because Humans can be appear in dierent postures like walking, sitting. Even some are seen in partially. So detecting those various kinds of humans is very challenging task. For this approach we use Microsoft depth camera to detect humans. So depth camera is gives depth images. This algorithm is scanning the depth images and identies the human in it. Mainly algorithm has three phases. 3.5.1 Depth image segmentation. Figure 3.10: Identifying the corresponding regions of distinct object in depth images Purpose of this step is identifying the corresponding regions of distinct object in depth images.(Figure 10) But resolution of depth images get poor with the distance. So that is not necessary to segment those images at larger distance. Accurate way is sub sample the depth image and then does the segmentation. 10

3.5.2 Region Filtering and Merging Figure 3.11: Image after omit unnecessary lines In this phase algorithm take Figure 10 as input and omit unnecessary lines which are highly unlikely humans and merge into the remaining regions.(Figure 10) 3.5.3 Candidate Classication In the nal step we use a support vector machine to decide whether the detected object human or not. 3.6 Obstacle detection using Equivalent points. Firstly we have to take a photo of an object by using two cameras this object will specify a particular place for itself in the pictures taken by cameras[6]. We named these points as equivalent points. If someone takes picture from dierent distance those places no longer equivalent. Then we have to convert the image into binary mode by changing value of pixels into 0 and 1. After that we separate equivalent region from others and blacken the rest of image. We should identify the object which we interested about and draw a box around them. Since we take those two images using two cameras there are regions that are exist in rst picture and not exist in second one. We have to cut those regions out. Since we are using two cameras in two positions to take the photo one might be bigger than other. So we have to stretch the images and make them standardize to comparable. Researcher has found three regions that object can exist. In this approach we can tell object whether exist or not and the region that obstacle exist. 11

3.7 Multi-cue Visual Obstacle Detection. Visual detection algorithms are mainly base on color and geometric information[7]. Color base only approaches are copiously gives false negatives and false positives. If there is a paper of another color on the oor robot will detect it as an obstacle. So it is a false positive. As well as if there is obstacle which is same color as oor robot will not detect it. It is false negative. Example instances have been mentioned in below Figure 12. Figure 3.12: False negative and false positive Even geometric only approaches can be given many false negative results. This ap- proach has two kinds of obstacle detectors both color base and geometric base. There are dierent approaches to detect them. Both algorithms will produce binary images that are consisting of white areas which represent the obstacles. 3.7.1 Geometric obstacle detection For do this we have to make an assumption that oor is nearly planar. In the algorithm two camera images are map in to another by 3d points. Algorithm determines a new position for each pixel point. Obstacle detection is done wrapping one of the images to other and comparing two images. But higher lightning conditions algorithm may give more false positive results. 3.7.2 Color base obstacle detection First of all we have to take several images of the ground and do the training process. Training is done selecting three region of the ground and all the result put into a three dimensional histogram. Classication process divides into two phases. In rst stage check every pixel with particular value in histogram. If value is less than the threshold detected as obstacle. After that those pixels are check again as a 4 * 4 pixel block. If those all pixels are checked as an obstacle then determine as an obstacle. 12

4 Discussion As I mentioned in chapter 03 there are many approaches to detect obstacle detection in images. These days there are lots of sensors are using in commercial devices like unmanned military aircrafts. But those sensors are very expensive. Because of that many devices have a pair of stereo cameras and most of the time running an ecient algorithm on stereo images. When I do the literature survey I identied few main obstacle detection algorithms that often use in applications. Obstacle detection using depth maps and disparity image is quit famous method among the researchers. When we got a Disparity images it should be ltered by a proper algorithm to reduce the noise. In [3] is use 2D EEMD algorithm to lter the noise. Problem is this method after two layers again it similar to rst layer. Then it is very dicult to user to identify the place of the object. Other than that user can gain more details from the depth map. User can determine the shape of the obstacle as well as the depth of the obstacle. But in high lightning area is not given very accurate results. Human detection application in [5] also uses depth image segmentation. Since it detect only human obstacles task is much harder than other approaches. But in the end both two applications success and obstacle detection is done with minimum error percentage. In mobile robot path planning application using 3d and 2D grid maps obstacle de- tection is done successfully than depth maps[1].Because Robot creates a 3D grid map all over the environment and build a 2d grid map according to that. In this case robot know not only his front but also he knows what are the objects that around him. Problem is when not complete the training process there may be some unknown objects around the robot. Other than that this is very ecient path planning approach to robots. Object detection using ltering is a not very ecient approach to real time appli- cations. Because it has many steps and can be given many false negative results. As well as it can be trained only for very few items at once. This approach use for mine explore robot in this literature survey[2].In the appearance model obstacle detection done by color detection. I think it very inecient approach to detect a mine. Because mines are existing in dierent colors. Shapes of the mines almost same. But again there can be so many exceptions. When your robot play with mine like dangerous things it should be 100% present accurate. Otherwise may be destroyed your application. 13

5 Conclusion As I mention earlier obstacle detection is most important topic in many elds. Among those elds robotic technology, unmanned aircrafts in military services and Applications that are helps to blind people are very important. I must say this is a much brought area in vision and recent past many researchers are involved to this elds to explore new methods and approaches. Although have many technics and algorithms to obstacle detection there are some suitable approaches depends on the application which are use them. In mobile robot area most of the time use 3D and 2D grid maps [1] and depth images[5]. I think most ecient way of path planning and obstacle avoidance technic is using the grid maps. Because in grid maps robot train for his current environment and he knows the all the obstacles. But only disadvantage is when we add a new obstacle in to the environment robot have to generate all the grid maps again. In depth images robot real time detect the obstacle and path his plan. But this approach much slower than grid maps. There are two approaches mainly discussed in applications about blind people. First one is using depth images[3]. I think this is a good approach because user can get all the information that which shape obstacle is and how many meters to the obstacles. Problems are the accuracy of the algorithm depends on image brightness and pixel size. Advantage is user can see the obstacle in two depths. When user knows the obstacle exist in two meters away he has a time to change his path. Other approach is using Equivalent Points. But this method detects only the obstacle. Depth can't be measured. So considering these two approaches you can realize using the depth map will be a better approach. 14

6 Future works This literature survey has described how obstacle detection done by using a camera. Besides of that there are so many obstacle detection sensors in the market. But they are very expensive at the moment. That's why many application running with stereo camera. For an example LADAR (Laser Detection and Ranging) sensors are using for un- manned aircrafts for emergency landing. They are scanning the whole area before landing the aircraft[12]. LADAR system using for determine the distance to an object. Benets of the LADAR system can get the image of the target while it is calculating the distance for the target. That functionality can use for obstacle detection purposes. 15

References [1] Atsushi Yamashita, Masaaki Kitaoka, and Toru Kaneko, 2011. Motion Planning of Biped Robot Equipped with Stereo Camera Using Grid Map. IEEE Transactions on Robotics and Automation, Vol.5 No.5, 639-647. [2] Stancil, Brian A., Hyams, Jerey, Shelley, Jordan; Babu, Kartik; Badino, Hernán.; Bansal, Aayush; Huber, Daniel; Batavia, Parag, 2013. CANINE: a robotic mine dog. Intelligent Robots and Computer Vision XXX: Algorithms and Techniques. Proceedings of the SPIE, Volume 8662, article id. 86620L, 12. [3] Paulo Costaa, Hugo Fernandesb, Paulo Martinsc, João Barrosod, Leontios J. Had- jileontiadise, (2012). Obstacle detection using stereo imaging to assist the navigation of visually impaired people. In 4th International Conference on Software Develop- ment for Enhancing Accessibility and Fighting Info-exclusion. Douro Region, Portu- gal, July 19-22, 2012. Portugal: Procedia Computer Science. 83-94. [4] Young Hoon Lee ,Tung-Sing Leung, Gérard Medioni, (2012). Real-time staircase detection from a wearable stereo system. In Pattern Recognition (ICPR), 2012 21st International Conference. Tsukuba, 11-15 Nov. 2012. 1-4. [5] Benjamin Choi, C etin Meric li, Joydeep Biswas, and Manuela Veloso, 2013. Fast Human Detection for Indoor Mobile Robots Using Depth Images. IEEE International Conference on Robotics and Automation (ICRA) [6] Nazli Mohajeri, Roozbeh Raste, Sabalan Daneshvar,(2011). An Obstacle Detection System for Blind People. In Proceedings of the World Congress on Engineering. London, U.K., July 6 - 8, 2011. WCE. [7] Luis J. Manso, Pablo Bustos, Pilar Bachiller and Jos´e Moreno 2010. Multi-cue Visual Obstacle Detection for Mobile Robots. journal of physical agents, Vol 4, 1 pg [8] Tomoyuki Mori and Sebastian Scherer, "First Results in Detecting and Avoiding Frontal Obstacles from a Monocular Camera for Micro Unmanned Aerial Vehicles," International Conference on Robotics and Automation, May, 2013 [9] A. Ess, B. Leibe, K. Schindler, L. van Gool1 , (2009). Moving Obstacle Detection in Highly Dynamic Scenes. In Robotics and Automation, 2009. ICRA '09. IEEE International Conference. Kobe, 12-17 May 2009. 56 - 63 pg. [10] Tarek El-Gaaly, Christopher Tomaszewski, Abhinav Valada, Prasanna Velagapudi, Balajee Kannan, and Paul Scerri, "Visual Obstacle Avoidance for Autonomous Wa- tercraft using Smartphones," Proceedings of the Autonomous Robots and Multirobot Systems workshop (ARMS 2013, at AAMAS 2013), May, 2013. 16

[11] Tarek El-Gaaly, Christopher Tomaszewski, Abhinav Valada, Prasanna Velagapudi, Balajee Kannan and Paul Scerri, (2012). A Practical Obstacle Detection System for Autonomous Orchard Vehicles. In 2012 IEEE/RSJ International Conference. Vilam- oura, Algarve, Portugal, October 7-12, 2012.. zzz: zz. 3391- 3399 pg. [12] Sebastian Scherer, Lyle Chamberlain, Sanjiv Singh, 2012. Autonomous landing at unprepared sites by a full-scale helicopter. Robotics and Autonomous Systems. [13] Saeid Fazli, Hajar Mohammadi D., Payman Moallem, 2010. An Advanced Stereo Vi- sion Based Obstacle Detection with a Robust Shadow Removal Technique. SOURCE World Academy of Science, Engineering & Technology, 43 vol, 699p. [14] 4. Heather Jones, Uland Wong, Kevin Peterson, Jason Koenig, Aashish Sheshadri, and William (Red) L. Whittaker, "Complementary Flyover and Rover Sensing for Superior Modeling of Planetary Features," Proceedings of the 8th International Con- ference on Field and Service Robotics, July, 2012. [15] Kostavelis, I, Nalpantidis, L & Gasteratos, A 2009, 'Real-Time Algorithm for Obsta- cle Avoidance Using a Stereoscopic Camera'. in Third Panhellenic Scientic Student Conference on Informatics. 17

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