Karl Berntorp

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This paper addresses the out-of-sequence measurement (OOSM) problem for mixed linear/nonlinear state-space models, which is a class of nonlinear models with a tractable, conditionally linear substructure. We develop two novel algorithms that utilize the linear substructure. The first algorithm effectively employs the Rao-Blackwellized particle filtering(More)
The position and orientation estimation problem for mobile robots is approached by fusing measurements from inertial sensors, wheel encoders, and a camera. The sensor fusion approach is based on the standard extended Kalman filter, which is modified to handle measurements from the camera with unknown prior delay. A real-time implementation is done on a(More)
  • Karl Berntorp
  • 2015 18th International Conference on Information…
  • 2015
Recent research has provided several new methods for avoiding degeneracy in particle filters. These methods implement Bayes' rule using a continuous transition between prior and posterior. The feedback particle filter (FPF) is one of them. The FPF uses feedback gains to adjust each particle according to the measurement, which is in contrast to conventional(More)
There is currently a strongly growing interest in obtaining optimal control solutions for vehicle maneuvers, both in order to understand optimal vehicle behavior and to devise improved safety systems, either by direct deployment of the solutions or by including mimicked driving techniques of professional drivers. However, it is nontrivial to find the right(More)
A particle filter based solution to the out-of-sequence measurement (OOSM) problem is proposed. The solution is storage efficient, while being computationally fast. The filter approaches the multi-OOSM problem by not only updating the estimate at the most recent time, but also for all times between the OOSM time and the most recent time. This is done by(More)
We investigate optimal maneuvers for road-vehicles on different surfaces such as asphalt, snow, and ice. The study is motivated by the desire to find control strategies for improved future vehicle safety and driver assistance technologies. Based on earlier presented measurements for tireforce characteristics, we develop tire models corresponding to(More)
There is currently a strongly growing interest in obtaining optimal control solutions for vehicle maneuvers, both in order to understand optimal vehicle behavior and, perhaps more importantly, to devise improved safety systems, either by direct deployment of the solutions or by including mimicked driving techniques of professional drivers. However, it is(More)
A comparative analysis shows how vehicle motion models of different complexity, capturing various characteristics, influence the solution when used in time-critical optimal maneuvering problems. Vehicle models with combinations of roll and pitch dynamics as well as load transfer are considered, ranging from a single-track model to a double-track model with(More)
We investigate the out-of-sequence measurements particle filtering problem for a set of conditionally linear Gaussian state-space models, known as mixed linear/nonlinear state-space models. Two different algorithms are proposed, which both exploit the conditionally linear substructure. The first approach is based on storing only a subset of the particles(More)