FastSLAM: An efficient solution to the simultaneous localization and mapping problem with unknown … (original) (raw)
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Journal of Intelligent and Robotic Systems, 2009
The process of building a map with a mobile robot is known as the Simultaneous Localization and Mapping (SLAM) problem, and is considered essential for achieving true autonomy. The best existing solutions to the SLAM problem are based on probabilistic techniques, mainly derived from the basic Bayes Filter. A recent approach is the use of Rao-Blackwellized particle filters. The FastSLAM solution factorizes the Bayes SLAM posterior using a particle filter to estimate over the possible paths of the robot and several independent Kalman Filters attached to each particle to estimate the location of landmarks conditioned to the robot path. Although there are several successful implementations of this idea, there is a lack of applications to indoor environments where the most common feature is the line segment corresponding to straight walls. This paper presents a novel factorization, which is the dual of the existing FastSLAM one, that decouples the SLAM into a map estimation and a localization problem, using a particle filter to estimate over maps and a Kalman Filter attached to each particle to estimate the robot pose conditioned to the given map. We have implemented and tested this approach, analyzing and comparing our solution with the FastSLAM one, and successfully building feature based maps of indoor environments.
A Review: Simultaneous Localization and Mapping Algorithms
Simultaneous Localization and Mapping (SLAM) involves creating an environmental map based on sensor data, while concurrently keeping track of the robot’s current position. Efficient and accurate SLAM is crucial for any mobile robot to perform robust navigation. It is also the keystone for higher-level tasks such as path planning and autonomous navigation. The past two decades have seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. In this paper, we will review the two common families of SLAM algorithms: Kalman filter with its variations and particle filters. This article complements other surveys in this field by reviewing the representative algorithms and the state-of-the-art in each family. It clearly identifies the inherent relationship between the state estimation via the KF versus PF techniques, all of which are derivations of Bayes rule.
Rapid Localization and Mapping Method Based on Adaptive Particle Filters
Sensors
With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an ...
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.
A New Particle Weighting Strategy for Robot Mapping FastSLAM
Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics, 2017
Nowadays, FastSLAM filters are the most widely used methods to solve the Simultaneous Localization and Mapping (SLAM) problem. In general, these approaches can use complex matrix formulation for computing the particle weighting procedure, during the execution of the SLAM algorithm. In this paper, we describe a new particle weight strategy for the FastSLAM filter, which can maintain the generation of particles in its most simplified form. Thus, this approach tries to estimate the robot poses and build the environment map using a simple geometric formulation for executing the particle weighting procedure. This method is capable of reducing the processing time and keeping the accuracy of the robot pose. Both simulation and experimental results demonstrate the feasibility of the proposed approach at enabling a robotic vehicle to accomplish the mapping of an unknown environment and also navigate through it.
Map-Aware Particle Filter for Localization
2018 IEEE International Conference on Robotics and Automation (ICRA), 2018
This work presents a method to improve vehicle localization by using the information from a prior occupancy grid to bound the possible poses. The method, named Map-Aware Particle Filter, uses a nonlinear approach to mapmatching that can be integrated into a particle filter framework for localization. Each particle is re-weighted based on the validity of its current position in the map. In addition, we buffer the trajectory followed by the vehicle and then append it to each particle's pose. We then quantify the overlap between the trajectory and the map's free space. This serves as a measure of each particle's validity given the trajectory and the shape of the map. We evaluated the method by performing experiments with different types of localization sensors: First, (i) we significantly reduced the drift inherent to dead reckoning. By only using wheel odometry and map information we achieved loop closure over a distance of approximately 3 km. We also (ii) increased the accuracy of GPS localization. Finally, (iii) we fused a fragile 2D LiDAR localization with the map information. The resulting system had a higher robustness and managed to close the loop in an outdated map where it had failed before.
Effcient simultaneous localisation and mapping in large and complex environments
2013
Finding its way in the environment in which a robot operates is a basic problem to solve for true autonomy. There are two main aspects to this problem, known as Simultaneous Localization and Mapping (SLAM): (1) the continuous problem of estimating the location of elements of interest for the robot, and (2) the discrete problem of finding correspondences between measurements of the sensor that the robot uses to perceive its environment and the elements already in the map. In this thesis we address these two aspects of SLAM. The estimation problem is classically solved with a filtering approach with satisfactory solutions for environments of limited size. But as the operating environments grow, basic filtering approaches become no more an option because of high computational cost and loss of precision. There has been great progress in advancing filtering algorithms in this sense. In this thesis we intend to push filtering algorithms further forward. We propose a highly scalable filter...
Simultaneous localization and mapping in multipath environments
2016 IEEE/ION Position, Location and Navigation Symposium (PLANS), 2016
This paper presents and extends the idea of multipath assisted positioning, named Channel-SLAM. Generally, multipath reception degrades the accuracy of the positioning device as long as the receiver is based on standard methods. In contrast, Channel-SLAM uses the multipath propagation of the wireless signal to allow positioning in cases of insufficient number of transmitters or increase the accuracy otherwise. Channel-SLAM treats multipath components (MPCs) as signals from virtual transmitters (VTs) which are time synchronized to the physical transmitter and fixed in their position. To use the information of the MPCs, Channel-SLAM estimates the receiver position and the position of the VTs simultaneously and does not require any prior information such as room-layout or a database for fingerprinting. The simultaneous localization and mapping (SLAM) algorithm is used by the receiver to estimate its own position and the position of VTs as landmarks. This paper investigates mapping of the receiver position, where we derive a probabilistic map representation based on locations. Thus, if the receiver knows its current location, we also know the probability where the receiver moves for the next step. In order to estimate and store the probability distribution of receivers motions as a function of location, we propose a probabilistic map that represents the receiver motion in a two-dimensional hexagonal grid. Hence, as soon as the receiver returns to an already mapped position, information of this position can be reused for positioning to obtain better position estimations of the receiver position. The algorithm is evaluated based on measurements with one fixed transmitter and a moving pedestrian which moves on partially overlapping loops. Based on these evaluations, we show, that the algorithm is able to accurately map the trajectory as well as reuse estimated map.
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.