HMM-Based Dynamic Mapping with Gaussian Random Fields (original) (raw)
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Mapping dynamic environments using Markov random field models
2018 24th International Conference on Automation and Computing (ICAC), 2018
This paper focuses on dynamic environments for mobile robots and proposes a new mapping method combining hidden Markov models (HMMs) and Markov random fields (MRFs). Grid cells are used to represent the dynamic environment. The state change of every grid cell is modelled by an HMM with an unknown transition matrix. MRFs are applied to consider the dependence between different transition matrices. The unknown parameters are learnt from not only the corresponding observations but also its neighbours. Given the dependence, parameter maps are smooth. Expectation Maximum (EM) is applied to obtain the best parameters from observations. Finally, a simulation is done to evaluate the proposed method.
Fully Bayesian Field Slam Using Gaussian Markov Random Fields
Asian Journal of Control, 2015
This paper presents a fully Bayesian way to solve the simultaneous localization and spatial prediction problem using a Gaussian Markov random field (GMRF) model. The objective is to simultaneously localize robotic sensors and predict a spatial field of interest using sequentially collected noisy observations by robotic sensors. The set of observations consists of the observed noisy positions of robotic sensing vehicles and noisy measurements of a spatial field. To be flexible, the spatial field of interest is modeled by a GMRF with uncertain hyperparameters. We derive an approximate Bayesian solution to the problem of computing the predictive inferences of the GMRF and the localization, taking into account observations, uncertain hyperparameters, measurement noise, kinematics of robotic sensors, and uncertain localization. The effectiveness of the proposed algorithm is illustrated by simulation results as well as by experiment results. The experiment results successfully show the flexibility and adaptability of our fully Bayesian approach in a data-driven fashion.
Statistical Inference in Mapping and Localization for Mobile Robots
Lecture Notes in Computer Science, 2004
In this paper we tackle the problem of providing a mobile robot with the ability to build a map of its environment using data gathered during navigation. The data correspond to the locations visited by the robot, obtained through a noisy odometer, and the distances to obstacles from each location, obtained from a noisy laser sensor. The map is represented as an occupancy grid. In this paper, we represent the process using a Graphical Representation based on a statistical structure resembling a Hidden Markov model. We determine the probability distributions involved in this Graphical Representation using a Motion Model, a Perception model, and a set of independent Bernoulli random variables associated with the cells in the occupancy grid forming the map. Our formulation of the problem leads naturally to the estimation of the posterior distribution over the space of possible maps given the data. We exploit a particular factorization of this distribution that allows us to implement an Importance Sampling algorithm. We show the results obtained by this algorithm when applied to a data set obtained by a robot navigating inside an office building type of indoor environment.
Sensors
In this paper, we present algorithms for predicting a spatio-temporal random field measured by mobile robotic sensors under uncertainties in localization and measurements. The spatio-temporal field of interest is modeled by a sum of a time-varying mean function and a Gaussian Markov random field (GMRF) with unknown hyperparameters. We first derive the exact Bayesian solution to the problem of computing the predictive inference of the random field, taking into account observations, uncertain hyperparameters, measurement noise, and uncertain localization in a fully Bayesian point of view. We show that the exact solution for uncertain localization is not scalable as the number of observations increases. To cope with this exponentially increasing complexity and to be usable for mobile sensor networks with limited resources, we propose a scalable approximation with a controllable trade-off between approximation error and complexity to the exact solution. The effectiveness of the proposed...
Gaussian random field-based log odds occupancy mapping
2018 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), 2018
This paper focuses on mapping problem with known robot pose in static environments and proposes a Gaussian random field-based log odds occupancy mapping (GRF-LOOM). In this method, occupancy probability is regarded as an unknown parameter and the dependence between parameters are considered. Given measurements and the dependence, the parameters of not only observed space but also unobserved space can be predicted. The occupancy probabilities in log odds form are regarded as a GRF. This mapping task can be solved by the wellknown prediction equation in Gaussian processes, which involves an inverse problem. Instead of the prediction equation, a new recursive algorithm is also proposed to avoid the inverse problem. Finally, the proposed method is evaluated in simulations. Index Terms-Binary Bayes filter, Gaussian random field, Log odds occupancy mapping.
International Journal of Automation and Computing, 2018
This paper models the complex simultaneous localization and mapping (SLAM) problem through a very flexible Markov random field and then solves it by using the iterated conditional modes algorithm. Markovian models allow to incorporate: any motion model; any observation model regardless of the type of sensor being chosen; prior information of the map through a map model; maps of diverse natures; sensor fusion weighted according to the accuracy. On the other hand, the iterated conditional modes algorithm is a probabilistic optimizer widely used for image processing which has not yet been used to solve the SLAM problem. This iterative solver has theoretical convergence regardless of the Markov random field chosen to model. Its initialization can be performed on-line and improved by parallel iterations whenever deemed appropriate. It can be used as a post-processing methodology if it is initialized with estimates obtained from another SLAM solver. The applied methodology can be easily implemented in other versions of the SLAM problem, such as the multi-robot version or the SLAM with dynamic environment. Simulations and real experiments show the flexibility and the excellent results of this proposal.
Mobile Robot Map Learning from Range Data in Dynamic Environments
Springer Tracts in Advanced Robotics, 2007
The problem of generating maps with mobile robots has received considerable attention over the past years. Most of the techniques developed so far have been designed for situations in which the environment is static during the mapping process. Dynamic objects, however, can lead to serious errors in the resulting maps such as spurious objects or misalignments due to localization errors. In this chapter, we consider the problem of creating maps with mobile robots in dynamic environments. We present two approaches to deal with non-static objects. The first approach interleaves mapping and localization with a probabilistic technique to identify spurious measurements. Measurements corresponding to dynamic objects are then filtered out during the registration process. Additionally, we present an approach that learns typical configurations of dynamic areas in the environment of a mobile robot. Our approach clusters local grid maps to identify the typical configurations. This knowledge is then used to improve the localization capabilities of a mobile vehicle acting in dynamic environments.
Mobile robot mapping in populated environments
Advanced Robotics, 2003
The problem of learning maps with mobile robots has received considerable attention over the past years. Most of the approaches, however, assume that the environment is static during the data-acquisition phase. In this paper we consider the problem of creating maps with mobile robots in populated environments. Our approach uses a probabilistic method to track multiple people and to incorporate the estimates of the tracking technique into the mapping process. The resulting maps are more accurate since the number of spurious objects is reduced and since the robustness of range registration is improved. Our approach has been implemented and tested on real robots in indoor and outdoor scenarios. We present several experiments illustrating the capabilities of our approach to generate accurate 2d and 3d maps.
Mobile Robot Navigation Based on Localisation Using Hidden Markov Models
araa.asn.au
In this paper, we implement a method of mobile robot naviga~ion using Hidden Markov Models (HMMs). It is a place-based navigation, in which the robot localises itself in previously learnt environment. We use the laser range data from the robot to scan the environment and to ...
Towards object mapping in non-stationary environments with mobile robots
2002
We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects change their locations over time. Virtually all existing environment mapping algorithms rely on a static world assumption, rendering them inapplicable to environments where things (chairs, desks, . . . ) move. A natural goal of robotics research, thus, is to learn models of nonstationary objects, and determine where they are at any point in time. This paper proposes an extension to the well-known occupancy grid mapping technique. Our approach uses a straightforward map differencing technique to detect changes in an environment over time. It employs the expectation maximization algorithm to learn models of non-stationary objects, and to determine the location of such objects in individual occupancy grid maps built at different points in time. By combining data from multiple maps when learning object models, the resulting models have higher fidelity than could be obtained from any single map. A Bayesian complexity measure is applied to determine the number of different objects in the model, making it possible to apply the approach to situations where not all objects are present at all times in the map.