Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed great practical success. This paper points out a ...
1 This paper describes two major student projects for the artificial intelligence course – Mapping using Bayesian filter and Monte Carlo Localization. These projects are also sui...
Myles F. McNally, Frank Klassner, Christopher Cont...
We present a new localization algorithm called Sensor Resetting Localization which is an extension of Monte Carlo Localization. The algorithm adds sensor based resampling to Monte...
In this paper, we describe a novel self-localizationalgorithm. Self-Localizationmethods are required for to lowercomputational costand handling vague sensordata. Thus, we propose ...
Abstract. We investigate the application of a Monte Carlo localization filter to the problem of combining local and global observations of a small, off-the-shelf quadruped domest...
Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sens...
Abstract. Self-localization in dynamic environments is a central problem in mobile robotics and is well studied in the literature. One of the most popular methods is the Monte Carl...
Andreas Strack, Alexander Ferrein, Gerhard Lakemey...
— In this paper we present a vision-based approach to self-localization that uses a novel scheme to integrate featurebased matching of panoramic images with Monte Carlo localizat...
- This paper introduces a new perceptual model for Monte Carlo Localization (MCL). In our approach a 3D laser scanner is used to observe the ceiling. The MCL matches ceiling struct...
— Range sensors are popular for localization since they directly measure the geometry of the local environment. Another distinct benefit is their typically high accuracy and spa...
Patrick Pfaff, Christian Plagemann, Wolfram Burgar...