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This work addresses range-only SLAM (RO-SLAM) as the Bayesian inference problem of sequentially tracking a vehicle while estimating the location of a set of beacons without any prior information. The only assumptions are the availability of odometry and a range sensor able of identifying the different beacons. We propose exploiting the conditional independence between the position distributions of...
The lack of a parameterized observation model in robot localization using occupancy grids requires the application of sampling-based methods, or particle filters. This work addresses the problem of optimal Bayesian filtering for dynamic systems with observation models that cannot be approximated properly as any parameterized distribution, which includes localization and SLAM with occupancy grids....
Range-only SLAM (RO-SLAM) represents a difficult problem due to the inherent ambiguity of localizing either the robot or the beacons from distance measurements only. Most previous approaches to this problem employ non-probabilistic batch optimizations or delay the initialization of new beacons within a probabilistic filter until a good estimate is available. The contribution of this work is the formulation...
This paper introduces a new approach to simultaneous localization and mapping (SLAM) that pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation that can cope with uncertainty in the measurements, the robot pose, and the map. Our approach is based on the reconstruction of the robot path in a hybrid...
One of the main elements of probabilistic localization and SLAM is the probabilistic sensor model (also known as the observation likelihood function). However, when dealing with very accurate sensors like laser range scanners, most approaches artificially inflate the uncertainty in the range measurements and assume conditional independence between the individual ranges of the scan to compute this...
Most successful works in simultaneous localization and mapping (SLAM) aim to build a metric map under a probabilistic viewpoint employing Bayesian filtering techniques. This work introduces a new hybrid metric-topological approach, where the aim is to reconstruct the path of the robot in a hybrid continuous-discrete state space which naturally combines metric and topological maps. Our fundamental...
In Bayesian based approaches to mobile robot simultaneous localization and mapping, Rao-Blackwellized particle filters (RBPF) enable the efficient estimation of the posterior belief over robot poses and the map. These particle filters have been recently adopted by many exploration approaches, to whom a central issue is measuring the certainty inherent to a given estimation in order to be able to select...
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