Robotic Mapping: A Survey (original) (raw)

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

1999

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach.

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.

Continuous Probabilistic Mapping by Autonomous Robots

1999

In this paper, we present a new approach for continuous probabilistic mapping. The objective is to build metric maps of unknown environments through cooperation between multiple autonomous mobile robots. The approach is based on a Bayesian update rule that can be used to integrate the range sensing data coming from multiple sensors on multiple robots. In addition, the algorithm is fast and computationally inexpensive so that it can be implemented on small robots with limited computation resources. The paper describes the algorithm and illustrates it with experiments in simulation and on real robots.

A statistical approach to simultaneous mapping and localization for mobile robots

The Annals of Applied Statistics, 2007

Mobile robots require basic information to navigate through an environment: they need to know where they are (localization) and they need to know where they are going. For the latter, robots need a map of the environment. Using sensors of a variety of forms, robots gather information as they move through an environment in order to build a map. In this paper we present a novel sampling algorithm to solving the simultaneous mapping and localization (SLAM) problem in indoor environments. We approach the problem from a Bayesian statistics perspective. The data correspond to a set of range finder and odometer measurements, obtained at discrete time instants. We focus on the estimation of the posterior distribution over the space of possible maps given the data. By exploiting different factorizations of this distribution, we derive three sampling algorithms based on importance sampling. We illustrate the results of our approach by testing the algorithms with two real data sets obtained through robot navigation inside office buildings at

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.

Map building with mobile robots in populated environments

2002

The problem of generating maps with mobile robots has received considerable attention over the past years. However, most of the approaches 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 results of the tracking technique into the mapping process. The resulting maps are more accurate since corrupted readings are treated accordingly during the matching phase and since the number of spurious objects in the resulting maps is reduced. Our approach has been implemented and tested on real robot systems in indoor and outdoor scenarios. We present several experiments illustrating the capabilities of our approach to generate accurate 2d and 3d maps.

Real-time indoor mapping for mobile robots with limited sensing

The 7th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2010), 2010

Mapping and localization for indoor robotic navigation is a well-studied field. However, existing work largely relies on long range perceptive sensors in addition to the robot's odometry, and little has been done with short range sensors. In this paper, we propose a method for real-time indoor mapping using only short range sensors such as bumpers and/or wall sensors that enable wall-following. The method uses odometry data from the robot's wall-following trajectory, together with readings from bumpers and wall sensors. The method first performs trace segmentation by fitting line segments to the noisy trajectory. Given the assumption of approximately rectilinear structure in the floor plans, typical for most indoor environments, a probabilistic rectification process is then applied to the segmented traces to obtain the orthogonal wall outlines. Both segmentation and rectification are performed on-line onboard the robot during its navigation through the environment. The resulting map is a set of line segments that represents the wall outline. The method has been tested in office buildings. Experimental results have shown that the method is robust to noisy odometry and non-rectilinear obstacles along the walls.

A cost effective probabilistic approach to localization and mapping

… , 2009. eit'09. IEEE …, 2009

Localization and mapping in robotics are preliminary but challenging problems. A learning approach must be followed by a robot to understand its environment and perform data association before it accomplishes any other tasks. In this paper, we describe a novel combination of techniques to map the environmental boundaries traced by the robot and localize it inside the bounded region. This is an effort established using only an iRobot educational package and no expensive high-end external sensor. This method may be treated as a solution for mapping and localization in a static environment with a few low cost IR sensors. In the proposed approach, we trace the robot's movement in an arbitrary shaped bounded region and map the same using coastal rule wall following technique and the method of least squares. A full traversal of robot maps the boundary and the robot is localized in the environment using particle filter approach and computational geometry. Also, we studied the effect of localizing a kidnapped robot once the map is known.

Semantic Mapping Using Mobile Robots

IEEE Transactions on Robotics, 2008

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.