Description

Book Synopsis
Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF).The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.

Table of Contents
Introduction; Sparse Information Filters in SLAM; Decoupling Localization and Mapping; D-SLAM Local Map Joining Filter; Sparse Local Submap Joining Filter.

Simultaneous Localization And Mapping: Exactly

    Product form

    £76.95

    Includes FREE delivery

    RRP £81.00 – you save £4.05 (5%)

    Order before 4pm tomorrow for delivery by Wed 5 Aug 2026.

    A Hardback by Zhan Wang, Shoudong Huang, Gamini Dissanayake

    Out of stock

      Trusted by thousands of customers. See 2,385+ Customer Reviews

      View other formats and editions of Simultaneous Localization And Mapping: Exactly by Zhan Wang

      Publisher: World Scientific Publishing Co Pte Ltd
      Publication Date: Publication Date: 02/06/2011
      ISBN13: 9789814350310, 978-9814350310
      ISBN10: 9814350311

      Description

      Book Synopsis
      Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF).The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.

      Table of Contents
      Introduction; Sparse Information Filters in SLAM; Decoupling Localization and Mapping; D-SLAM Local Map Joining Filter; Sparse Local Submap Joining Filter.

      Recently viewed products

      © 2026 Book Curl

        • American Express
        • Apple Pay
        • Diners Club
        • Discover
        • Google Pay
        • Maestro
        • Mastercard
        • PayPal
        • Shop Pay
        • Union Pay
        • Visa

        Login

        Forgot your password?

        Don't have an account yet?
        Create account