Authors

• Kara, Süleyman Fatih (Aselsan Inc.)
• Basaran, Burak (Aselsan Inc.)

Abstract

With their serious impact on the safe and economic operation of railway domains, train positioning systems play a crucial part in railway signalling. In this paper, we present a solution for such a train positioning system by making use of a tachometer, a Doppler radar and a magnetic positioning sensor (a.k.a tag). An IMM (Interacting Multiple Model) filter based sensor fusion algorithm has been used to calculate the velocity and position of the train using the above sensors. The algorithm has been developed with SCADE (Safety Critical Application Development Environment) which is a tool frequently used for development in safety-critical systems because it drastically simplifies and accelerates the certification process required of EN 50128.

Presentation

Authors

• Maric, Bruno (University of Zagreb, Faculty of Electrical Engineering and Comp)
• Polic, Marsela (University of Zagreb)
• Tabak, Tomislav (University of Zagreb)
• Orsag, Matko (University of Zagreb, Faculty of Electrical Engineering and Comp)

Abstract

In this work we are proposing an intuitive tool based on motion capture system for programming by demonstration tasks in robot manipulation. For a robot manipulator set in a working environment equipped with any external measurement system, we propose an online calibration method based on unsupervised learning and simplex optimization. Without loos of generality the Nelder-Mead simplex method is used to calibrate the rigid transforms of the robot tools and environment based on motion capture system recordings. Fast optimization procedure is enabled through dataset subsampling using iterative clustering and outlier detection procedure. The online calibration enables customization and execution of programming by demonstration tasks in real time.

Presentation

Authors

• Williams, Jason (CSIRO)
• Jiang, Shu (Georgia Institute of Technology)
• O’Brien, Matthew (Georgia Institute of Technology)
• Wagner, Glenn (Emesent)
• Hernandez, Emili (Emesent)
• Cox, Mark (CSIRO)
• Pitt, Alex (CSIRO)
• Arkin, Ronald (Georgia Tech)
• Hudson, Nicolas (X, The Moonshot Factory)

Abstract

While robots have long been proposed as a tool to reduce human personnel’s exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.

Presentation

Authors

• Maric, Wise, Emmett (University of Toronto)
• Giamou, Matthew (University of Toronto)
• Khoubyarian, Soroush (University of Toronto)
• Grover, Abhinav (University of Toronto)
• Kelly, Jonathan (University of Toronto)

Abstract

Correct fusion of data from two sensors requires an accurate estimate of their relative pose, which can be determined through the process of extrinsic calibration. When the sensors are capable of producing their own egomotion estimates (i.e., measurements of their trajectories through an environment), the `hand-eye’ formulation of extrinsic calibration can be employed. In this paper, we extend our recent work on a convex optimization approach for hand-eye calibration to the case where one of the sensors cannot observe the scale of its translational motion (e.g., a monocular camera observing an unmapped environment). We prove that our technique is able to provide a certifiably globally optimal solution to both the known- and unknown-scale variants of hand-eye calibration, provided that the measurement noise is bounded. Herein, we focus on the theoretical aspects of the problem, show the tightness and stability of our convex relaxation, and demonstrate the optimality and speed of our algorithm through experiments with synthetic data.

Authors

• Lindgren, David (Swedish Defence Research Agency, FOI)
• Nordzell, Andreas (Springbreeze AB)

Abstract

Doppler shift measurements on opportunistic radio sources can be an alternative to GNSS in disturbed environments. Mobile measurements on a GSM base station indicate that the uncertainty is sufficiently low for vehicle positioning, provided that at least two sources are within range and that measurements are fused with an odometer and a rate gyro. A key idea is to fuse the relatively uncertain Doppler measurements with accurate measurements of the vehicle speed. The positioning performance is analyzed by Monte Carlo simulations. A position RMSE in the interval 15-44m can be expected in a suburban environment with limited occlusion.

Presentation

Authors

• Mehami, Jasprabhjit (University of Technology Sydney)
• Vidal-Calleja, Teresa A. (University of Technology Sydney)
• Alempijevic, Alen (University of Technology Sydney)

Abstract

Multi-modal sensors such as hyperspectral line-scan and frame cameras can be incorporated into a single camera system, enabling individual sensor limitations to be compensated. Calibration of such systems is crucial to ensure data from one modality can be related to the other. The best known approach is to capture multiple measurements of a known planar pattern, which are then used to optimize calibration parameters through non-linear least squares. The confidence in the optimized parameters is dependent on the measurements, which are contaminated by noise due to sensor hardware. Understanding how this noise transfers through the calibration is essential, especially when dealing with line-scan cameras that rely on measurements to extract feature points. This paper adopts a maximum likelihood estimation method for propagating measurement noise through the calibration, such that the optimized parameters are associated with an estimate of uncertainty. The uncertainty enables development of an active calibration algorithm, which uses observability to selectively choose images that improve parameter estimation. The algorithm is tested in both simulation and hardware, then compared to a naive approach that uses all images to calibrate. The simulation results for the algorithm show a drop of 26.4% in the total normalized error and 46.8% in the covariance trace. Results from the hardware experiments also show a decrease in the covariance trace, demonstrating the importance of selecting good measurements for parameter estimation.

Presentation

Authors

• Kara, Süleyman Fatih (Aselsan Inc.)
• Basaran, Burak (Aselsan Inc.)

Abstract

With their serious impact on the safe and economic operation of railway domains, train positioning systems play a crucial part in railway signalling. In this paper, we present a solution for such a train positioning system by making use of a tachometer, a Doppler radar and a magnetic positioning sensor (a.k.a tag). An IMM (Interacting Multiple Model) filter based sensor fusion algorithm has been used to calculate the velocity and position of the train using the above sensors. The algorithm has been developed with SCADE (Safety Critical Application Development Environment) which is a tool frequently used for development in safety-critical systems because it drastically simplifies and accelerates the certification process required of EN 50128.

Presentation

Authors

• Maric, Bruno (University of Zagreb, Faculty of Electrical Engineering and Comp)
• Polic, Marsela (University of Zagreb)
• Tabak, Tomislav (University of Zagreb)
• Orsag, Matko (University of Zagreb, Faculty of Electrical Engineering and Comp)

Abstract

In this work we are proposing an intuitive tool based on motion capture system for programming by demonstration tasks in robot manipulation. For a robot manipulator set in a working environment equipped with any external measurement system, we propose an online calibration method based on unsupervised learning and simplex optimization. Without loos of generality the Nelder-Mead simplex method is used to calibrate the rigid transforms of the robot tools and environment based on motion capture system recordings. Fast optimization procedure is enabled through dataset subsampling using iterative clustering and outlier detection procedure. The online calibration enables customization and execution of programming by demonstration tasks in real time.

Presentation

Authors

• Williams, Jason (CSIRO)
• Jiang, Shu (Georgia Institute of Technology)
• O’Brien, Matthew (Georgia Institute of Technology)
• Wagner, Glenn (Emesent)
• Hernandez, Emili (Emesent)
• Cox, Mark (CSIRO)
• Pitt, Alex (CSIRO)
• Arkin, Ronald (Georgia Tech)
• Hudson, Nicolas (X, The Moonshot Factory)

Abstract

While robots have long been proposed as a tool to reduce human personnel’s exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.

Presentation

Authors

• Maric, Wise, Emmett (University of Toronto)
• Giamou, Matthew (University of Toronto)
• Khoubyarian, Soroush (University of Toronto)
• Grover, Abhinav (University of Toronto)
• Kelly, Jonathan (University of Toronto)

Abstract

Correct fusion of data from two sensors requires an accurate estimate of their relative pose, which can be determined through the process of extrinsic calibration. When the sensors are capable of producing their own egomotion estimates (i.e., measurements of their trajectories through an environment), the `hand-eye’ formulation of extrinsic calibration can be employed. In this paper, we extend our recent work on a convex optimization approach for hand-eye calibration to the case where one of the sensors cannot observe the scale of its translational motion (e.g., a monocular camera observing an unmapped environment). We prove that our technique is able to provide a certifiably globally optimal solution to both the known- and unknown-scale variants of hand-eye calibration, provided that the measurement noise is bounded. Herein, we focus on the theoretical aspects of the problem, show the tightness and stability of our convex relaxation, and demonstrate the optimality and speed of our algorithm through experiments with synthetic data.

Authors

• Lindgren, David (Swedish Defence Research Agency, FOI)
• Nordzell, Andreas (Springbreeze AB)

Abstract

Doppler shift measurements on opportunistic radio sources can be an alternative to GNSS in disturbed environments. Mobile measurements on a GSM base station indicate that the uncertainty is sufficiently low for vehicle positioning, provided that at least two sources are within range and that measurements are fused with an odometer and a rate gyro. A key idea is to fuse the relatively uncertain Doppler measurements with accurate measurements of the vehicle speed. The positioning performance is analyzed by Monte Carlo simulations. A position RMSE in the interval 15-44m can be expected in a suburban environment with limited occlusion.

Presentation

Authors

• Mehami, Jasprabhjit (University of Technology Sydney)
• Vidal-Calleja, Teresa A. (University of Technology Sydney)
• Alempijevic, Alen (University of Technology Sydney)

Abstract

Multi-modal sensors such as hyperspectral line-scan and frame cameras can be incorporated into a single camera system, enabling individual sensor limitations to be compensated. Calibration of such systems is crucial to ensure data from one modality can be related to the other. The best known approach is to capture multiple measurements of a known planar pattern, which are then used to optimize calibration parameters through non-linear least squares. The confidence in the optimized parameters is dependent on the measurements, which are contaminated by noise due to sensor hardware. Understanding how this noise transfers through the calibration is essential, especially when dealing with line-scan cameras that rely on measurements to extract feature points. This paper adopts a maximum likelihood estimation method for propagating measurement noise through the calibration, such that the optimized parameters are associated with an estimate of uncertainty. The uncertainty enables development of an active calibration algorithm, which uses observability to selectively choose images that improve parameter estimation. The algorithm is tested in both simulation and hardware, then compared to a naive approach that uses all images to calibrate. The simulation results for the algorithm show a drop of 26.4% in the total normalized error and 46.8% in the covariance trace. Results from the hardware experiments also show a decrease in the covariance trace, demonstrating the importance of selecting good measurements for parameter estimation.

Presentation