A solution to the simultaneous localization and map building (SLAM) problem (original) (raw)

A Discussion of Simultaneous Localization and Mapping

Autonomous Robots, 2006

This paper aims at a discussion of the structure of the SLAM problem. The analysis is not strictly formal but based both on informal studies and mathematical derivation. The first part highlights the structure of uncertainty of an estimated map with the key result being "Certainty of Relations despite Uncertainty of Positions". A formal proof for approximate sparsity of so-called information matrices occurring in SLAM is sketched. It supports the above mentioned characterization and provides a foundation for algorithms based on sparse information matrices. Further, issues of nonlinearity and the duality between information and covariance matrices are discussed and related to common methods for solving SLAM. Finally, three requirements concerning map quality, storage space and computation time an ideal SLAM solution should have are proposed. The current state of the art is discussed with respect to these requirements including a formal specification of the term "map quality".

Simultaneous Localization and Mapping - A Discussion

This papers provides two contributions to the problem of Simultaneous Localization and Mapping (SLAM): First we discuss properties of the problem itself and of the intended semantics of an uncertain map representation, with the main idea of "representing certainty of relations despite the uncertainty of positions". We propose some requirements an ideal solution of SLAM should have concerning uncertainty, memory space and computation time and discuss existing approaches in the light of these requirements. The second part proposes a representation based on sparse information matrices together with some properties that motivate this approach. This is shown to comply to the uncertainty and space requirements. To derive an estimated map from the representation a sparse linear equation system has to be solved. However, an update of the representation itself needs only constant time, making it highly attractive for building a SLAM algorithm.

Map building and SLAM algorithms

2006

The concept of autonomy of mobile robots encompasses many areas of knowledge, methods and ultimately algorithms designed for trajectory control, obstacle avoidance, localization, map building, and so forth. Practically, the success of a path planning and navigation mission of an autonomous vehicle depends on the availability of both a sufficiently reliable estimation of the vehicle location, and an accurate representation of the navigation area.

An analysis of the bias correction problem in simultaneous localization and mapping

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005

Unmodeled systematic and nonsystematic errors in robot kinematics and measurement processes often cause adverse effects in several autonomous navigation tasks. In particular, accumulated sensor biases can render simultaneous localization and mapping (SLAM) algorithms of autonomous vehicles to perform very poorly especially in large unexplored terrains including cycles, as a result of the estimator divergence and inconsistency. One way to deal with this problem is the accurate modeling and precise calibration of sensors. However this may add up to longer setup and calibration times. Even after accurate calibration and modeling, sensor calibration may often subject to drifts, rendering the efforts ineffective. Therefore, the correct and effective way to deal with this problem is explicit estimation of these parameters with other states. In this work we address the estimation theoretic sensor bias correction problem in SLAM using a simple unified framework and establish theoretically, the behavior and properties of the solution with special consideration to diminishing uncertainty, rates of convergence and observability.

Simultaneous Localization and Mapping-Literature Survey

This paper presents the state of the art in Simultaneous Localization and Mapping. SLAM has been a constant research subject for the last thirty years due to the necessity of determining the position and the path of a robot in an uncertain environment. The following paper addresses different methodologies for implementing solutions to the SLAM problem. In particular we plan to research the simultaneous localization and mapping of an autonomous vehicle with ultrasonic sensor and camera [2], [3], [4]. This paper presents also two new optimized algorithms: the differential evolution algorithm and the Unscented FastSLAM algorithm for determining the position and direction of a conveyance device that is moving in uncertain surroundings. Furthermore we will review a proposed method for measuring the accuracy of the most known techniques used in determining the trajectory of an autonomous robot in an unknown environment. It appears that there is no definite solution to our problem even if the research has been ongoing for the past three decades in this field. Due to the numerous uncertainties and the computational complications that arise from these methods it becomes obvious that when dealing with large spaces and moving objects there isn't an established solution to mapping and consequently the trajectory of the robot is still variant upon the environment.

Real-Time Radar SLAM

2017

The Simultaneous Localization and Mapping (SLAM) problem is one of the key problems on the way to autonomous driving. This paper provides a cost-efficient and robust method with great accuracy in both localization and mapping. Therefore, a particle based localization algorithm combined with 2D occupancy grid mapping is used. The algorithm uses an odometer to obtain information about the vehicle movement and four radar sensors to get a 360◦ coverage of the environment. The algorithm is evaluated on a dynamically changing parking lot scenario and a driveway scenario. For each scenario, the algorithm is compared with a highly accurate ground truth system. In certain situations, the algorithm achieves a RMS error of less than 0.2 m. The results prove the performance of the algorithm.

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

IEEE Transactions on Automation Science and Engineering, 2001

This work 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 filter 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 O(N a 2 ), where N 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.

The Simultaneous Localization and Mapping (SLAM)-An Overview

Surveying and Geospatial Engineering Journal

Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which...

Hybrid architecture for simultaneous localization and map building in large outdoor areas

International Conference on Intelligent RObots and Systems - IROS, 2002

This paper address the problem of navigating in very large outdoor unstructured environments. It presents solutions to the problem of closing large loops in simultaneous localization and map building applications. A hybrid architecture is presented that make use of the Extended Kalman Filter to perform SLAM in an efficient form and a Monte Carlo type filter to resolve the data association problem present when closing large loops. The proposed algorithm incorporates integrity to the standard SLAM algorithms by allowing multimode distribution to be handled in real time. Experimental results in outdoor environments are also presented. 0-7803-7398-7/02/$17.00 ©2002 IEEE

The SLAM problem: A survey

2008

This paper surveys the most recent published techniques in the field of Simultaneous Localization and Mapping (SLAM). In particular it is focused on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios. The main research field we plan to investigate is the filtering algorithms as a way of reducing the amount of data. It seems that almost all the current approaches can not perform consistent maps for large areas, mainly due to the increase of the computational cost and due to the uncertainties that become prohibitive when the scenario becomes larger.