mobile robot motions often originate from an uninformed path-sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive co...
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mobile robot motions often originate from an uninformed path-sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive collision-testing process. Although all necessary information for collision testing a path is known to the planner, that information is typically stored in a relatively unavailable form in a costmap or obstacle map. By summarizing the most salient data in a more accessible form, our process delivers a denser sampling of the free path space per unit time than open-loop sampling techniques. We obtain this result by probabilistically modeling-in real time and with minimal information-the locations of obstacles and free space, based on collision-test results. We present CALM, the combined adaptive locality model, along with an algorithm to bias path sampling based on the model's predictions. We provide experimental results in simulation for motion planning on mobile robots, demonstrating up to a 330% increase in paths surviving collision test.
The vast amount of data robots can capture today motivates the development of fast and scalable statistical tools to model the space the robot operates in. We devise a new technique for environment representation thro...
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The vast amount of data robots can capture today motivates the development of fast and scalable statistical tools to model the space the robot operates in. We devise a new technique for environment representation through continuous occupancy mapping that improves on the popular occupancy grip maps in two fundamental aspects: (1) it does not assume an a priori discrimination of the world into grid cells and therefore can provide maps at an arbitrary resolution;(2) it captures spatial relationships between measurements naturally, thus being more robust to outliers and possessing better generalization performance. The technique, named Hilbert maps, is based on the computation of fast kernel approximations that project the data in a Hilbert space where a logistic regression classifier is learnt. We show that this approach allows for efficient stochastic gradient optimization where each measurement is only processed once during learning in an online manner. We present results with three types of approximations: random Fourier;Nystrom;and a novel sparse projection. We also extend the approach to accept probability distributions as inputs, for example, due to uncertainty over the position of laser scans due to sensor or localization errors. In this extended version, experiments were conducted in two dimensions and three dimensions, using popular benchmark datasets. Furthermore, an analysis of the adaptive capabilities of the technique to handle large changes in the data, such as trajectory update before and after loop closure during simultaneous localization and mapping, is also included.
The vast amount of data robots can capture today motivates the development of fast and scalable statistical tools to model the space the robot operates in. We devise a new technique for environment representation thro...
详细信息
The vast amount of data robots can capture today motivates the development of fast and scalable statistical tools to model the space the robot operates in. We devise a new technique for environment representation through continuous occupancy mapping that improves on the popular occupancy grip maps in two fundamental aspects: (1) it does not assume an a priori discrimination of the world into grid cells and therefore can provide maps at an arbitrary resolution;(2) it captures spatial relationships between measurements naturally, thus being more robust to outliers and possessing better generalization performance. The technique, named Hilbert maps, is based on the computation of fast kernel approximations that project the data in a Hilbert space where a logistic regression classifier is learnt. We show that this approach allows for efficient stochastic gradient optimization where each measurement is only processed once during learning in an online manner. We present results with three types of approximations: random Fourier;Nystrom;and a novel sparse projection. We also extend the approach to accept probability distributions as inputs, for example, due to uncertainty over the position of laser scans due to sensor or localization errors. In this extended version, experiments were conducted in two dimensions and three dimensions, using popular benchmark datasets. Furthermore, an analysis of the adaptive capabilities of the technique to handle large changes in the data, such as trajectory update before and after loop closure during simultaneous localization and mapping, is also included.
mobile robot motions often originate from an uninformed path-sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive co...
详细信息
mobile robot motions often originate from an uninformed path-sampling process such as random or low-dispersion sampling. We demonstrate an alternative approach to path sampling that closes the loop on the expensive collision-testing process. Although all necessary information for collision testing a path is known to the planner, that information is typically stored in a relatively unavailable form in a costmap or obstacle map. By summarizing the most salient data in a more accessible form, our process delivers a denser sampling of the free path space per unit time than open-loop sampling techniques. We obtain this result by probabilistically modeling-in real time and with minimal information-the locations of obstacles and free space, based on collision-test results. We present CALM, the combined adaptive locality model, along with an algorithm to bias path sampling based on the model's predictions. We provide experimental results in simulation for motion planning on mobile robots, demonstrating up to a 330% increase in paths surviving collision test.
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