Trajectory reconstruction by integration of GPS and a swarm of MEMS accelerometers: model and analysis of observability

In this paper, we consider the problem of trajectory reconstrucion for the mobile mapping system Davide. Davide is equipped with a custom GPS/INS system and a number of dead-reckoning sensors, such as odometers,inclinometers, etc. In this paper, we investigate the possibility of substituting the INS with a swarm of low cost MEMS accelerometers located in various positions on board the vehicle. An observability analysis shows the feasibility of the solution and the need of integrating the distributed MEMS sensing architecture with a positioning sensor such as the GPS

Trajectory reconstruction by integration of GPS and a swarm of MEMS accelerometers: model and analysis of observability

We consider the problem of trajectory reconstruction for the mobile mapping system Davide. Davide is equipped with a custom GPS/INS system and a number of dead-reckoning sensors, such as odometers, inclinometers, etc. We investigate the possibility of substituting the INS with a swarm of low cost MEMS accelerometers located in various positions on board the vehicle. An observability analysis shows the feasibility of the solution and the need of integrating the distributed MEMS sensing architecture with a positioning sensor such as the GPS

Model based GPS/INS integration for high accuracy land vehicle applications

In this paper, we consider the problem of trajectory reconstruction for the mobile mapping system Davide. Davide is equipped with a custom GPS/INS system and a number of dead-reckoning sensors, such as odometers, inclinometers, etc. The application falls in the framework of state reconstruction with multi-sensor architectures, for which GPS/INS integration is a paradigmatic problem. In this paper, to maximize the accuracy of trajectory reconstruction, we propose to use a combination of two strategies, namely: a) to use a customized dynamical model of the vehicle, that includes nonholonomic constraints and load transfer in roll and pitch, and b) to use advanced nonlinear filtering techniques such as Montecarlo filters. The performance of proposed algorithm is illustrated by means of both simulations and real vehicle data

Model based GPS/INS integration for high accuracy land vehicle applications

In this paper, we consider the problem of trajectory reconstruction for the mobile mapping system Davide. Davide is equipped with a custom GPS/INS system and a number of dead-reckoning sensors, such as odometers, inclinometers, etc. The application falls in the framework of state reconstruction with multi-sensor architectures, for which GPSnNS integration is a paradigmatic problem. In this paper, to maximize the accuracy of trajectory reconstruction, we propose to use a combination oftwo strategies, namely a) to use a customized dynamical model of the vehicle, that includes nonholonomic constraints and load transfer in roll and pitch, and b) to use advanced nonlinear filtering techniques such as Montecarto filters.The performance of proposed algorithm is illustrated by means of both simulations and real vehicle data