Episodic Non-Markov Localization: Reasoning About Short-Term and Long-Term Features

Markov localization and its variants are widely used for localization of mobile robots. These methods assume Markov independence of observations, implying that observations made by a robot correspond to a static map. However, in real human environments, observations include occlusions due to unmapped objects like chairs and tables, and dynamic objects like humans. We introduce an episodic non-Markov localization algorithm that maintains estimates of the belief over the trajectory of the robot while explicitly reasoning about observations and their correlations arising from unmapped static objects, moving objects, as well as objects from the static map. Observations are classified as arising from longterm features, short-term features, or dynamic features, which correspond to mapped objects, unmapped static objects, and unmapped dynamic objects respectively. By detecting time steps along the robot’s trajectory where unmapped observations prior to such time steps are unrelated to those afterwards, nonMarkov localization limits the history of observations and pose estimates to “episodes” over which the belief is computed. We demonstrate non-Markov localization in challenging real world indoor and outdoor environments over multiple datasets, comparing it with alternative state-of-the-art approaches, showing it to be robust as well as accurate

Monte Carlo Localization in Hand-Drawn Maps

Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the correct room with a robustness up to 80

Convergence Analysis of MCMC Method in the Study of Genetic Linkage with Missing Data

Computational infeasibility of exact methods for solving genetic linkage analysis problems has led to the development of a new collection of stochastic methods, all of which require the use of Markov chains. The purpose of this work is to investigate the complexities of missing data in pedigree analysis using the Monte Carlo Markov Chain (MCMC) method as compared to the exact results. Also, we attempt to determine an association between missing data in a familial pedigree and the convergence to stationarity of a descent graph Markov chain implemented in the stochastic method for parametric linkage analysis. In particular, we will implement the stochastic method to solve a pedigree problem for a disease trait, in order to look at the associated problems with missing data from the pedigree, and investigate the deviation between the MCMC method and the exact results. Using the method for maximum autocorrelation and bounding of the second largest eigenvalue, we will study the effects of missing data on the convergence rate and the accuracy of the MCMC method in solving the pedigree analysis problem. Finally, we will use the computational implementation of SimWalk2 to study the convergence rate and accuracy of the MCMC method for the disease Episodic Ataxia. The implementation of the MCMC method through SimWalk2 for the disease gene Episodic Ataxia found evidence to suggest that both the efficiency and accuracy of the method may be severely reduced by an increase in missing data in the pedigree. Certain variations of model parameters influenced the ability of the method to produce accurate results, but the most crucial of the variables studied was the level of missing information from the pedigree itself. This can be seen as a detriment to the implementation, as pedigree information is very often missing from the model. Further research in this topic would need to include the implementation of this method on more genetic parameters and differing pedigree variations. Also, it might be of interest to look into possible ways to combat the effects of missing data on the MCMC method

Toward an object-based semantic memory for long-term operation of mobile service robots

Throughout a lifetime of operation, a mobile service robot needs to acquire, store and update its knowledge of a working environment. This includes the ability to identify and track objects in different places, as well as using this information for interaction with humans. This paper introduces a long-term updating mechanism, inspired by the modal model of human memory, to enable a mobile robot to maintain its knowledge of a changing environment. The memory model is integrated with a hybrid map that represents the global topology and local geometry of the environment, as well as the respective 3D location of objects. We aim to enable the robot to use this knowledge to help humans by suggesting the most likely locations of specific objects in its map. An experiment using omni-directional vision demonstrates the ability to track the movements of several objects in a dynamic environment over an extended period of time