Reference number : GC-2017-Q1-02824, amendment number : , agreement type : G, recipient legal name : WANG, JIANGUO (York University)|WANG, JIANGUO (Université York), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : With the increasing availability of low-cost sensors (MEMS IMUs, cameras, LiDARs etc.), the challenge is how to optimally integrate them to reach accurate and reliable navigation solution in challenging environments, e.g., GNSS degraded or denied environments. Meanwhile, the increasing demand for high quality multisensor integrated navigation is continually driving more and more research and development activities and applications. Hence, the long-term goal of this research is to develop the emerging data fusion methodology in multisensor integrated navigation for challenging environments. Its focus is placed on innovating multisensor integration strategies in consideration of rapid technological progress and economic productivity, enhancing system accuracy and reliability for the growing needs of seamless indoor and outdoor navigation, and exploring new applications.
 
 The long-term research goal will be achieved through fulfilling the proposed short-term objectives. First, the general multisensor integration strategy, developed during the last Discover Grant Program, has made possible for the sensor measurements to directly participate in Kalman filter measurement updates without distinguishing between the core IMU and other aiding sensors by employing a kinematic model of a moving platform as the core of the system model. This is paramount to the utilization of low-cost sensors, e.g., MEMS IMUS. However, for its practicability, this generic multisensor integration strategy will further be improved and optimized through theoretical and practical enhancements such as effective reduction of high rate measurement updates, optimal state selections, smooth transitions between alternative kinematic models, solution evaluation at system level, and error analysis at sensor level. Second, the investigation of the potential of low-cost IMU arrays will be conducted by modeling them individually and developing low-cost IMU auto calibration methodology. Third, we will develop LiDAR-visual odometry to further enhance multisensor (GNSS receivers/IMUs/Cameras) integrated navigation for the better navigation solution in challenging environments, and further a framework for a complementarily-integrated solution of trajectory determination and 3D mapping.
 
 This program will greatly contribute to their advancement by its significance of a practically applicable generic multisensor integration strategy, the new approach to model IMU arrays and the low-cost IMU auto calibration process and a framework for a complementarily-integrated solution of trajectory determination and 3D mapping. The success of this research program will greatly impact on meeting the great challenges due to the benefits of low cost sensors and the complication in challenging working environments and improving the lack of highly demanded high-qualified personnel in this area to some extent, program purpose french : With the increasing availability of low-cost sensors (MEMS IMUs, cameras, LiDARs etc.), the challenge is how to optimally integrate them to reach accurate and reliable navigation solution in challenging environments, e.g., GNSS degraded or denied environments. Meanwhile, the increasing demand for high quality multisensor integrated navigation is continually driving more and more research and development activities and applications. Hence, the long-term goal of this research is to develop the emerging data fusion methodology in multisensor integrated navigation for challenging environments. Its focus is placed on innovating multisensor integration strategies in consideration of rapid technological progress and economic productivity, enhancing system accuracy and reliability for the growing needs of seamless indoor and outdoor navigation, and exploring new applications.
 
 The long-term research goal will be achieved through fulfilling the proposed short-term objectives. First, the general multisensor integration strategy, developed during the last Discover Grant Program, has made possible for the sensor measurements to directly participate in Kalman filter measurement updates without distinguishing between the core IMU and other aiding sensors by employing a kinematic model of a moving platform as the core of the system model. This is paramount to the utilization of low-cost sensors, e.g., MEMS IMUS. However, for its practicability, this generic multisensor integration strategy will further be improved and optimized through theoretical and practical enhancements such as effective reduction of high rate measurement updates, optimal state selections, smooth transitions between alternative kinematic models, solution evaluation at system level, and error analysis at sensor level. Second, the investigation of the potential of low-cost IMU arrays will be conducted by modeling them individually and developing low-cost IMU auto calibration methodology. Third, we will develop LiDAR-visual odometry to further enhance multisensor (GNSS receivers/IMUs/Cameras) integrated navigation for the better navigation solution in challenging environments, and further a framework for a complementarily-integrated solution of trajectory determination and 3D mapping.
 
 This program will greatly contribute to their advancement by its significance of a practically applicable generic multisensor integration strategy, the new approach to model IMU arrays and the low-cost IMU auto calibration process and a framework for a complementarily-integrated solution of trajectory determination and 3D mapping. The success of this research program will greatly impact on meeting the great challenges due to the benefits of low cost sensors and the complication in challenging working environments and improving the lack of highly demanded high-qualified personnel in this area to some extent, agreement title english : Emerging data fusion methodology in multisensor integrated navigation for challenging environments, agreement title french : Emerging data fusion methodology in multisensor integrated navigation for challenging environments, agreement number : RGPIN, agreement value : 120000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada