Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6385739
Chao-Lin Wu, Mao-Yung Weng, Ching-Hu Lu, L. Fu
Human activity is among the critical information for a context-aware smart home since knowing what activities are undertaken is important for providing appropriate services. Most of the prior works primarily focus on recognizing individual activity, thus requiring high cost to track people and performs not well when there are multiple users, which is common in a real home environment. Therefore, we propose hierarchical generalized context inference to infer multi-user contexts. By treating a multi-user context as a generalized context caused by an aggregated entity, our approach generalizes these multi-user contexts with different information granularity, and then dynamically infers and aggregates these generalized contexts. Based on the inference results of generalized contexts, a context-aware smart home can provide appropriate services as much as possible. Our experimental results demonstrate the effectiveness of the proposed approach.
{"title":"Hierarchical generalized context inference or context-aware smart homes","authors":"Chao-Lin Wu, Mao-Yung Weng, Ching-Hu Lu, L. Fu","doi":"10.1109/IROS.2012.6385739","DOIUrl":"https://doi.org/10.1109/IROS.2012.6385739","url":null,"abstract":"Human activity is among the critical information for a context-aware smart home since knowing what activities are undertaken is important for providing appropriate services. Most of the prior works primarily focus on recognizing individual activity, thus requiring high cost to track people and performs not well when there are multiple users, which is common in a real home environment. Therefore, we propose hierarchical generalized context inference to infer multi-user contexts. By treating a multi-user context as a generalized context caused by an aggregated entity, our approach generalizes these multi-user contexts with different information granularity, and then dynamically infers and aggregates these generalized contexts. Based on the inference results of generalized contexts, a context-aware smart home can provide appropriate services as much as possible. Our experimental results demonstrate the effectiveness of the proposed approach.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"26 1","pages":"5227-5232"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83120029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6385549
Lei Shi, S. Kodagoda, G. Dissanayake
Representation of spaces including both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Therefore, in recent years identifying and semantically labeling the environments based on onboard sensors has become an important competency for mobile robots. Supervised learning algorithms have been extensively used for this purpose with SVM-based solutions showing good generalization properties. The CRF-based approaches take the advantage of connectivity information of samples thereby provide a mechanism to capture complex dependencies. Blending the complementary strengths of Support Vector Machine (SVM) and Conditional Random Field (CRF), there have been algorithms to exploit the advantages of both to enhance the overall accuracy of place classification in indoor environments. However, experiments show that none of the above approaches deal well with diversified testing data. In this paper, we focus mainly on the generalization ability of the model and propose a semi-supervised learning strategy, which essentially improves the performance of the system. Experiments have been carried out on six real-world maps from different universities around the world and the results from rigorous testing demonstrate the feasibility of the approach.
{"title":"Application of semi-supervised learning with Voronoi Graph for place classification","authors":"Lei Shi, S. Kodagoda, G. Dissanayake","doi":"10.1109/IROS.2012.6385549","DOIUrl":"https://doi.org/10.1109/IROS.2012.6385549","url":null,"abstract":"Representation of spaces including both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Therefore, in recent years identifying and semantically labeling the environments based on onboard sensors has become an important competency for mobile robots. Supervised learning algorithms have been extensively used for this purpose with SVM-based solutions showing good generalization properties. The CRF-based approaches take the advantage of connectivity information of samples thereby provide a mechanism to capture complex dependencies. Blending the complementary strengths of Support Vector Machine (SVM) and Conditional Random Field (CRF), there have been algorithms to exploit the advantages of both to enhance the overall accuracy of place classification in indoor environments. However, experiments show that none of the above approaches deal well with diversified testing data. In this paper, we focus mainly on the generalization ability of the model and propose a semi-supervised learning strategy, which essentially improves the performance of the system. Experiments have been carried out on six real-world maps from different universities around the world and the results from rigorous testing demonstrate the feasibility of the approach.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"1 1","pages":"2991-2996"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83389598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6385685
F. Shkurti, Anqi Xu, Malika Meghjani, J. A. G. Higuera, Yogesh A. Girdhar, P. Giguère, Bir Bikram Dey, J. Li, A. Kalmbach, C. Prahacs, Katrine Turgeon, Ioannis M. Rekleitis, G. Dudek
In this paper we describe a heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect visual footage of interesting underwater regions, from multiple scales and mediums. We discuss organizational and scheduling complexities associated with multi-robot experiments in a field robotics setting. We also present results from our field trials, where we demonstrated the use of this heterogeneous robot team to achieve multi-domain monitoring of coral reefs, based on real-time interaction with a remotely-located marine biologist.
{"title":"Multi-domain monitoring of marine environments using a heterogeneous robot team","authors":"F. Shkurti, Anqi Xu, Malika Meghjani, J. A. G. Higuera, Yogesh A. Girdhar, P. Giguère, Bir Bikram Dey, J. Li, A. Kalmbach, C. Prahacs, Katrine Turgeon, Ioannis M. Rekleitis, G. Dudek","doi":"10.1109/IROS.2012.6385685","DOIUrl":"https://doi.org/10.1109/IROS.2012.6385685","url":null,"abstract":"In this paper we describe a heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect visual footage of interesting underwater regions, from multiple scales and mediums. We discuss organizational and scheduling complexities associated with multi-robot experiments in a field robotics setting. We also present results from our field trials, where we demonstrated the use of this heterogeneous robot team to achieve multi-domain monitoring of coral reefs, based on real-time interaction with a remotely-located marine biologist.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"3 1","pages":"1747-1753"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83406563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6385786
I. Galiana, Frank L. Hammond, R. Howe, Marko B. Popovic
Stroke is the leading cause of long-term disability in the United States, affecting over 795,000 people annually. In order to regain motor function of the upper body, patients are usually treated by regular sessions with a dedicated physical therapist. A cost-effective wearable upper body orthotics system that can be used at home to empower both the patients and physical therapists is described. The system is composed of a thin, compliant, lightweight, cost-effective soft orthotic device with an integrated cable actuation system that is worn over the upper body, an embedded limb position sensing system, an electric actuator package and controller. The proposed device is robust to misalignments that may occur during actuation of the compliant brace or when putting on the system. Through simulations and experimental evaluation, it was demonstrated i) that the soft orthotic cable-driven shoulder brace can be successfully actuated without the production of off-axis torques in the presence of misalignments and ii) that the proposed model can identify linear and angular misalignments online.
{"title":"Wearable soft robotic device for post-stroke shoulder rehabilitation: Identifying misalignments","authors":"I. Galiana, Frank L. Hammond, R. Howe, Marko B. Popovic","doi":"10.1109/IROS.2012.6385786","DOIUrl":"https://doi.org/10.1109/IROS.2012.6385786","url":null,"abstract":"Stroke is the leading cause of long-term disability in the United States, affecting over 795,000 people annually. In order to regain motor function of the upper body, patients are usually treated by regular sessions with a dedicated physical therapist. A cost-effective wearable upper body orthotics system that can be used at home to empower both the patients and physical therapists is described. The system is composed of a thin, compliant, lightweight, cost-effective soft orthotic device with an integrated cable actuation system that is worn over the upper body, an embedded limb position sensing system, an electric actuator package and controller. The proposed device is robust to misalignments that may occur during actuation of the compliant brace or when putting on the system. Through simulations and experimental evaluation, it was demonstrated i) that the soft orthotic cable-driven shoulder brace can be successfully actuated without the production of off-axis torques in the presence of misalignments and ii) that the proposed model can identify linear and angular misalignments online.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"4 1","pages":"317-322"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82216250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6385497
Javier V. Gómez, David Álvarez, S. Garrido, L. Moreno
This paper presents a novel robotic learning technique based on Fast Marching Square (FM2). This method, which we have called FM Learning, is based on incorporating previous experience to the path planning system of the robot by taking into account paths taught to the robot via kinesthetic teaching, this is, guiding manually the robot through the desired path. The method proposed ensures that the path planning is always a globally asymptotically stable system at the target point, considering the motion as a nonlinear autonomous dynamical system. The few parameters the algorithm has can be tuned to get different behaviours of the learning system. The method has been evaluated through a set of simulations and also tested in the mobile manipulator Manfred V2.
{"title":"Kinesthetic teaching via Fast Marching Square","authors":"Javier V. Gómez, David Álvarez, S. Garrido, L. Moreno","doi":"10.1109/IROS.2012.6385497","DOIUrl":"https://doi.org/10.1109/IROS.2012.6385497","url":null,"abstract":"This paper presents a novel robotic learning technique based on Fast Marching Square (FM2). This method, which we have called FM Learning, is based on incorporating previous experience to the path planning system of the robot by taking into account paths taught to the robot via kinesthetic teaching, this is, guiding manually the robot through the desired path. The method proposed ensures that the path planning is always a globally asymptotically stable system at the target point, considering the motion as a nonlinear autonomous dynamical system. The few parameters the algorithm has can be tuned to get different behaviours of the learning system. The method has been evaluated through a set of simulations and also tested in the mobile manipulator Manfred V2.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"25 1","pages":"1305-1310"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82226329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6385911
Dongjun Lee, Myungsin Kim, T. Qiu
We consider the problem of passive haptic rendering and interfacing of multiple degree-of-freedom (DOF) virtual proxy (VP), which has nonlinear Lagrangian dynamics and interacts with deformable virtual objects. For this, we solve the followings: 1) how to extend our recently-proposed non-iterative passive mechanical integrator (NPMI [1]) to simulate this nonlinear Lagrangian VP haptically-fast and discrete-time passively; 2) how to utilize virtual coupling technique to interface this NPMI-simulated VP and haptic devices while guaranteeing (sampled-data) passivity; and 3) how to passively render the interaction between VP and linear visco-elastic deformable virtual objects, while enhancing passivity at contact-on/off switchings. Some experimental results are also presented to support theory.
{"title":"Passive haptic rendering and control of Lagrangian virtual proxy","authors":"Dongjun Lee, Myungsin Kim, T. Qiu","doi":"10.1109/IROS.2012.6385911","DOIUrl":"https://doi.org/10.1109/IROS.2012.6385911","url":null,"abstract":"We consider the problem of passive haptic rendering and interfacing of multiple degree-of-freedom (DOF) virtual proxy (VP), which has nonlinear Lagrangian dynamics and interacts with deformable virtual objects. For this, we solve the followings: 1) how to extend our recently-proposed non-iterative passive mechanical integrator (NPMI [1]) to simulate this nonlinear Lagrangian VP haptically-fast and discrete-time passively; 2) how to utilize virtual coupling technique to interface this NPMI-simulated VP and haptic devices while guaranteeing (sampled-data) passivity; and 3) how to passively render the interaction between VP and linear visco-elastic deformable virtual objects, while enhancing passivity at contact-on/off switchings. Some experimental results are also presented to support theory.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"33 1","pages":"64-69"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81290191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6385466
Jun Zhang, G. Song, Z. Li, Guifang Qiao, Hongtao Sun, Aiguo Song
This paper presents a 9 cm × 7 cm × 12 cm, 154 g jumping robot with self-righting, steering, and takeoff angle adjusting capabilities. The quick energy releasing function of the jumping mechanism is implemented by using an eccentric cam. The self-righting, steering, and takeoff angle adjusting capabilities are achieved by adding a rotatable pole leg. The pole leg can prop up the body of the robot when it falls down. The pole leg can also steer the robot to turn at a step of about 24°. By adjusting the center of mass (COM), the robot can jump at different takeoff angles. Experimental results show that the constructed robot can jump more than 88 cm high at a takeoff angle of 82.7° and it can continuously jump to overcome stairs.
{"title":"Self-righting, steering and takeoff angle adjusting for a jumping robot","authors":"Jun Zhang, G. Song, Z. Li, Guifang Qiao, Hongtao Sun, Aiguo Song","doi":"10.1109/IROS.2012.6385466","DOIUrl":"https://doi.org/10.1109/IROS.2012.6385466","url":null,"abstract":"This paper presents a 9 cm × 7 cm × 12 cm, 154 g jumping robot with self-righting, steering, and takeoff angle adjusting capabilities. The quick energy releasing function of the jumping mechanism is implemented by using an eccentric cam. The self-righting, steering, and takeoff angle adjusting capabilities are achieved by adding a rotatable pole leg. The pole leg can prop up the body of the robot when it falls down. The pole leg can also steer the robot to turn at a step of about 24°. By adjusting the center of mass (COM), the robot can jump at different takeoff angles. Experimental results show that the constructed robot can jump more than 88 cm high at a takeoff angle of 82.7° and it can continuously jump to overcome stairs.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"9 1","pages":"2089-2094"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81314270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6386274
Carmen Lopera, H. Tome, A. Tsouroukdissian, F. Stulp
When first posed with the problem 15 × 15, we may generate the answer by applying a set of rules, e.g breaking the problem down into (10 + 5) × 15 and solving the subcomponents of these simpler multiplications first [2]. But after having solved this problem several times, we simply recall that the answer to 15 × 15 is 225. This distinction between generation and recall can also be applied to motor planning [2], as described in the next two sections.
{"title":"Comparing motion generation and motion recall for everyday mobile manipulation tasks","authors":"Carmen Lopera, H. Tome, A. Tsouroukdissian, F. Stulp","doi":"10.1109/IROS.2012.6386274","DOIUrl":"https://doi.org/10.1109/IROS.2012.6386274","url":null,"abstract":"When first posed with the problem 15 × 15, we may generate the answer by applying a set of rules, e.g breaking the problem down into (10 + 5) × 15 and solving the subcomponents of these simpler multiplications first [2]. But after having solved this problem several times, we simply recall that the answer to 15 × 15 is 225. This distinction between generation and recall can also be applied to motor planning [2], as described in the next two sections.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"15 1","pages":"3045-3046"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81357323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6385473
Pratik Agarwal, Edwin Olson
State of the art methods for state estimation and perception make use of least-squares optimization methods to perform efficient inference on noisy sensor data. Much of this efficiency is achieved by using sparse matrix factorization methods. The sparsity structure of the underlying matrix factorization which makes these optimization methods tractable is highly dependent on the choice of variable reordering; but there has been no systematic evaluation of reordering methods in the SLAM community. In this paper we evaluate the performance of various reordering techniques on benchmark SLAM data sets and provide definitive recommendations based on our results. We also compare these state of the art algorithms against our simple and easy to implement algorithm which achieves comparable performance. Finally, we provide empirical evidence that few gains remain with respect to variants of minimum degree ordering.
{"title":"Variable reordering strategies for SLAM","authors":"Pratik Agarwal, Edwin Olson","doi":"10.1109/IROS.2012.6385473","DOIUrl":"https://doi.org/10.1109/IROS.2012.6385473","url":null,"abstract":"State of the art methods for state estimation and perception make use of least-squares optimization methods to perform efficient inference on noisy sensor data. Much of this efficiency is achieved by using sparse matrix factorization methods. The sparsity structure of the underlying matrix factorization which makes these optimization methods tractable is highly dependent on the choice of variable reordering; but there has been no systematic evaluation of reordering methods in the SLAM community. In this paper we evaluate the performance of various reordering techniques on benchmark SLAM data sets and provide definitive recommendations based on our results. We also compare these state of the art algorithms against our simple and easy to implement algorithm which achieves comparable performance. Finally, we provide empirical evidence that few gains remain with respect to variants of minimum degree ordering.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"3 1","pages":"3844-3850"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82401361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-24DOI: 10.1109/IROS.2012.6386281
Markus Achtelik, M. Achtelik, Y. Brunet, M. Chli, S. Chatzichristofis, J. Decotignie, K. Doth, F. Fraundorfer, L. Kneip, Daniel Gurdan, Lionel Heng, E. Kosmatopoulos, L. Doitsidis, Gim Hee Lee, Simon Lynen, Agostino Martinelli, Lorenz Meier, M. Pollefeys, D. Piguet, A. Renzaglia, D. Scaramuzza, R. Siegwart, J. Stumpf, Petri Tanskanen, C. Troiani, S. Weiss
The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.
{"title":"SFly: Swarm of micro flying robots","authors":"Markus Achtelik, M. Achtelik, Y. Brunet, M. Chli, S. Chatzichristofis, J. Decotignie, K. Doth, F. Fraundorfer, L. Kneip, Daniel Gurdan, Lionel Heng, E. Kosmatopoulos, L. Doitsidis, Gim Hee Lee, Simon Lynen, Agostino Martinelli, Lorenz Meier, M. Pollefeys, D. Piguet, A. Renzaglia, D. Scaramuzza, R. Siegwart, J. Stumpf, Petri Tanskanen, C. Troiani, S. Weiss","doi":"10.1109/IROS.2012.6386281","DOIUrl":"https://doi.org/10.1109/IROS.2012.6386281","url":null,"abstract":"The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.","PeriodicalId":6358,"journal":{"name":"2012 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"111 1","pages":"2649-2650"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80961907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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