Addresses real-time implementation of the simultaneous localization and map-building (SLAM) algorithm. It presents optimal algorithms that consider the special form of the matrices and a new compressed filler that can significantly reduce the computation requirements when working in local areas or with high frequency external sensors. It is shown that by extending the standard Kalman filter models the information gained in a local area can be maintained with a cost /spl sim/O(N/sub a//sup 2/), where N/sub a/ is the number of landmarks in the local area, and then transferred to the overall map in only one iteration at full SLAM computational cost. Additional simplifications are also presented that are very close to optimal when an appropriate map representation is used. Finally the algorithms are validated with experimental results obtained with a standard vehicle running in a completely unstructured outdoor environment.

/pdf/optimization-of-the-simultaneous-localization-and-map-4si7pg0575.pdf

Optimization of the simultaneous localization and map-building algorithm for real-time implementation

This paper investigates the convergence properties and consistency of extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point landmarks observed using a range-and-bearing sensor. It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates. This paper also provides some insights to the inconsistencies of EKF based SLAM that have been recently observed. The fundamental cause of EKF SLAM inconsistency for two basic scenarios are clearly stated and associated theoretical proofs are provided.

/pdf/convergence-and-consistency-analysis-for-extended-kalman-rlx37ywue6.pdf

Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM

This paper proposes an approach to calibrate off-the-shelf cameras and inertial sensors to have a useful integrated system to be used in static and dynamic situations. When both sensors are integrated in a system their relative pose needs to be determined. The rotation between the camera and the inertial sensor can be estimated, concurrently with camera calibration, by having both sensors observe the vertical direction in several poses. The camera relies on a vertical chequered planar target and the inertial sensor on gravity to obtain a vertical reference. Depending on the setup and system motion, the translation between the two sensors can also be important. Using a simple passive turntable and static images, the translation can be estimated. The system needs to be placed in several poses and adjusted to turn about the inertial sensor centre, so that the lever arm to the camera can be determined. Simulation and real data results are presented to show the validity and simple requirements of the proposed methods.

Relative Pose Calibration Between Visual and Inertial Sensors

Mining accidents have occurred since the early days of mining. Therewere a total of 525 mining disasters (incidents with five or more fatalities) in both coal and metal/nonmetal mines from 1900 through 2007 in the United States, resulting in 12,823 fatalities. Most of these disasters involve mine rescue teams, which are specially trained to perform search and rescue operations in extremely hostile environments. Robots have a great potential to assist in these underground operations, searching ahead of rescue teams and reporting conditions that may be hazardous to the teams. When explosive conditions exist or when heavy smoke or unstable ground conditions prevent team members from entering a mine, robots can become an invaluable tool.

Mobile robots in mine rescue and recovery

This paper presents an algorithm to find the line-based map that best fits sets of two-dimensional range scan data. To construct the map, we first provide an accurate means to fit a line segment to a set of uncertain points via maximum likelihood formalism. This scheme weights each point's influence on the fit according to its uncertainty, which is derived from sensor noise models. We also provide closed-form formulas for the covariance of the line fit, along with methods to transform line coordinates and covariances across robot poses. A Chi-squared based criterion for "knitting" together sufficiently similar lines can be used to merge lines directly (as we demonstrate) or as part of the framework for a line-based SLAM implementation. Experiments using a Sick LMS-200 laser scanner and a Nomad 200 mobile robot illustrate the effectiveness of the algorithm.

/pdf/weighted-line-fitting-algorithms-for-mobile-robot-map-cem2zbji4o.pdf

Weighted line fitting algorithms for mobile robot map building and efficient data representation

Presents a closed form solution to the estimation-theoretic simultaneous localisation and map building (SLAM) problem. The solution is obtained by explicit solution of the differential Riccati equation associated with the n-landmark SLAM problem. The solution describes and explains the many experimental and theoretical results obtained so far in the study of the SLAM problem. Further, the solution, for the first time, allows a precise means of analysing the performance of different SLAM algorithms and enables the design of efficient SLAM systems.

http://ieeexplore.ieee.org/iel5/7285/19696/00912757.pdf

A closed form solution to the single degree of freedom simultaneous localisation and map building (SLAM) problem

The paper describes a terrain-aided navigation system that employs points of maximum curvature extracted from laser scan data as primary landmarks. A scale space method is used to extract points of maximum curvature from laser range scans of unmodified outdoor environments. This information is then fused with odometric information to provide localization information for an outdoor vehicle. The method described is invariant to the size and orientation of the range images under consideration (with respect to rotation and translation), is robust to noise, and can reliably detect and localize naturally occurring landmarks in the operating environment. The algorithm is demonstrated in the application of a road vehicle in an unmodified operating domain.

Natural landmark-based autonomous navigation using curvature scale space

This paper reports on the map-building and map-based localization of a load-haul-dump (LHD) truck in an underground mine using statistical pattern-matching techniques utilizing range images obtained from a scanning laser range-finder The map-building approach is based on an extended Kalman filter (EKF) and the resulting map is composed of poly-lines. Three approaches are proposed for the localization of the vehicle, namely the iterative closest point (ICP) approach, a reflective beacon based approach and the combined ICP-EKF approach, wherein, the last two approaches explicitly take into account the uncertainty associated with the observation data. These approaches are then compared using data gathered from an underground mine in Queensland, Australia for their relative merits subject to various factors and the corresponding results are presented.

Map-building and map-based localization in an underground-mine by statistical pattern matching

This work presents an efficient initial calibration and alignment algorithm for a six-degree of freedom inertial navigation unit. The individual error models for the gyros and accelerometers are presented with a study of its effects in trajectory prediction. A full error model is also presented to determine the sensors needed for full observability of the different perturbation parameters. Finally, dead reckoning experimental results are presented based on the initial alignment and calibration parameters. The results show that the algorithm proposed is able to obtain accurate position and velocity information for a significant period of time using an inertial measurement unit as the only sensor.

Initial calibration and alignment of an inertial navigation

This paper reviews the state-of-the-art in the automation of underground mining vehicles and reports on the development of an autonomous navigation system under development through the CMTE with sponsorship arranged by AMIRA Past attempts at automating LHDs and haul trucks are described and their particular strengths and weaknesses are discussed The auto-guidance system being developed overcomes some of the limitations of state-of-the-art prototype aecommercialAE systems It can be retrofitted to existing remote controlled vehicles, uses minimum installed infrastructure and is flexible enough for rapid relocation to alternate routes The navigation techniques use data fusion of two separate sets of sensors combining natural feature recognition, nodal maps and inertial navigation techniques Collision detection is incorporated and people and other traffic are excluded from the tramming area This paper describes the work being done by the group with regard to auto-tramming and also outlines the future goals

H. Durrant-Whyte

Papers

A closed form solution to the single degree of freedom simultaneous localisation and map building (SLAM) problem

Natural landmark-based autonomous navigation using curvature scale space

Map-building and map-based localization in an underground-mine by statistical pattern matching

Initial calibration and alignment of an inertial navigation

Automation of underground LHD and truck haulage