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The representation of probabilistic graphical model often encodes a network whose size is unboundedly large. Such networks pose particular challenges to inference algorithms, specifically making the task of robot path queries highly inefficient due to poor locality of memory references. Whereas a more predictable, resolution complete method yields a highly compact graph structure that captures much...
Time-of-Flight (ToF) cameras gain depth information by emitting amplitude-modulated near-infrared light and measuring the phase shift between the emitted and the reflected signal. The phase shift is proportional to the object's distance modulo the wavelength of the modulation frequency. This results in a distance ambiguity. Distances larger than the wavelength are wrapped into the sensor's non-ambiguity...
To interact naturally with humans, robots need to be aware of their own surroundings. This awareness is usually encoded in some implicit or explicit representation of the situated context. In this paper, we present a new framework for constructing rich belief models of the robot's environment. Key to our approach is the use of Markov Logic as a unified framework for inference over these beliefs. Markov...
This paper proposes a novel method for computing robot motion uncertainty from ranging sensor data. The method utilises the recently proposed CRF-Matching procedure which matches laser scans based on shape descriptors. Motion estimates are computed in a probabilistic framework by performing inference on a probabilistic graphical model. We propose an efficient sampling procedure for obtaining probable...
This paper presents a novel framework for integrating fundamental tasks in robotic navigation through a statistical inference procedure. A probabilistic model that jointly reasons about scan-matching, moving object detection and their motion estimation is developed. Scan-matching and moving object detection are two important problems for full autonomy of robotic systems in complex dynamic environments...
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