The SPmap: a probabilistic framework for simultaneous localization and map building (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.

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

Sensor influence in the performance of simultaneous mobile robot localization and map building

Experimental Robotics VI, 2000

Mobile robot navigation in unknown environments requires the concurrent estimation of the mobile robot localization with respect to a base reference and the construction of a global map of the navigation area. In this paper we present a comparative study of the performance of the localization and map building processes using two distinct sensorial systems: a rotating 2D laser rangefinder, and a trinocular stereo vision system.

Simultaneous localization and map building for mobile robot navigation

IEEE Robotics & Automation Magazine, 1999

ne of the key objectives in mobile robotics is to gjve an autonomous mobile robot the ability to perform various tasks in an indoor structured environment. In or-<'U der to do that, thr robot has to becomc aware" ofthe environment and its position in it. To achieve that, a localization method that does not intervene with the rnvironment with beacons or marken and is not influenced by unexpected objects has to be integrated with a map-building algorithm that builds and constantly updates the aorld model using sensor data. While in motion, frequent sampling results in a map representation that is approptiatr for modeling inaccurate and noisy range-sensor data, such as those produced by ultrasonic sensors Results on the localization and niaphuildmg problems have been previously presented, but v e~ little effort has been made to deal simultaneously with both problems.

Simultaneous Localization and Mapping for Mobile Robots

2013

Common SE(2) and SE(3) Geometric Operations Dealing with mobile robots necessarily implies dealing with geometric problems. Studying their kinematic models or their position and attitude in three-dimensional space, for example, requires us to handle spatial relationships. This appendix provides a summary of the basic mathematical concepts from 2D and 3D geometry that are needed for solving most mobile robotic problems. Some other mathematically more intricate concepts will be deferred until appendix IV.

2D Simultaneous Localization And Mapping

A common challenge for autonomous robots is the Simultaneous Localization and Mapping (SLAM) problem: given an unknown environment, can the robot simultaneously generate a map of its surroundings and locate itself in this map? In this project, a solution to the SLAM problem was implemented on a Pioneer 1 robot equipped with a SICK laser scanner.

SLAM based on quantities invariant of the robot's configuration

2004

This paper presents a solution to the Simultaneous Localization and Mapping (SLAM) problem in the stochastic map framework for a mobile robot navigating in an indoor environment. The approach is based on the concept of the relative map. The idea consists in introducing a map state, which only contains quantities invariant under translation and rotation. In this way the landmark estimation is decoupled from the robot motion and therefore the estimation does not rely on the unmodeled error sources of the robot motion. A new landmark is introduced by considering the intersection point between two lines. Only landmarks whose position error is small are considered. In this way the intersection point is the natural extension of the corner feature. The relative state estimated through a Kalman filter contains the distances among the intersection points observed at the same time. Real experiments carried out with a mobile robot equipped with a 360 o laser range finder show the performance of the approach.

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.