Model based Kalman Filter Mobile Robot Self-Localization

Ivanjko, Edouard; Kitanov, Andreja; Petrović, Ivan

doi:10.5772/9256

Cited by 10 publications

(8 citation statements)

References 18 publications

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“…For standard MCL with SICK laser scanners, position errors between 0.05 m and 0.2 m are reported in [8] and [25]. There exist more recent reports on EKF/UKF-based pose estimation errors for a Pioneer2 robot using SICK laser rangefinders where the error is around 0.25 m and 3.5 deg [17]-these errors appear to be rather large and their source is not really clear from our perspective. Brščić and Hashimoto report errors below "0.1 m at all times, while being less than 0.05 m most of the times" in [3] for a similar hardware setup.…”

Section: Related Workmentioning

confidence: 96%

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On the position accuracy of mobile robot localization based on particle filters combined with scan matching

Röwekämper

Sprunk

Tipaldi

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

113

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Abstract-Many applications in mobile robotics and especially industrial applications require that the robot has a precise estimate about its pose. In this paper, we analyze the accuracy of an integrated laser-based robot pose estimation and positioning system for mobile platforms. For our analysis, we used a highly accurate motion capture system to precisely determine the error in the robot's pose. We are able to show that by combining standard components such as Monte-Carlo localization, KLD sampling, and scan matching, an accuracy of a few millimeters at taught-in reference locations can be achieved. We believe that this is an important analysis for developers of robotic applications in which pose accuracy matters.

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Section: Related Workmentioning

confidence: 96%

“…Commonly used sensors for vehicle localization are cameras [1], [2], [10], [15], [22], RFID or wireless receivers estimating radio signal strength [11], [9], laser scanners [8], [17] or GPS receivers. Vision-based MCL was first introduced by Dellaert et al [7].…”

Section: Related Workmentioning

confidence: 99%

On the position accuracy of mobile robot localization based on particle filters combined with scan matching

Röwekämper

Sprunk

Tipaldi

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

113

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“…These physical robots follow the differential drive robot model, i.e., the turning radius is more responsive and tighter than other drive models [13]. The kinematic difference equations that represent the robot's model are [14].…”

Section: Experimental Platformmentioning

confidence: 99%

Validation of swarms of robots: theory and experimental results

Méndez

Givigi

Schwartz

et al. 2012

2012 7th International Conference on System of Systems Engineering (SoSE)

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In this paper we present a formulation of a swarm algorithm for some predefined formations and we show that using the control laws derived, the whole system is stable. Furthermore, the algorithm for the formation is implemented in microcontrollers, which effectively makes the experiment a system of systems. The only input for the algorithm is the position of the robot and the relative positions of neighbours within two metres of the robot. The results of experiments presented show that the algorithm presents the same general behaviour as predicted in simulations. However, due to noise in measurements and commands, the behaviour is slightly different.

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“…In particular, in industrial environments, most industrial robots are equipped with lidars to sense the environment, avoid obstacles, and apply the brakes. In the literature [4,5], a standard Monte Carlo positioning method is used on the SICK Lidar with a localization error of about 0.05 m to 0.2 m. In the literature [6], a localization algorithm based on extended Kalman filter is used on the Pioneer2 robot with SICK Lidar; the pose error is approximately 0.25 m, 3.5°.…”

Section: Introductionmentioning

confidence: 99%

An Improved AMCL Algorithm Based on Laser Scanning Match in a Complex and Unstructured Environment

Peng

Zheng

et al. 2018

Complexity

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Adaptive Monte Carlo localization (AMCL) algorithm has a limited pose accuracy because of the nonconvexity of the laser sensor model, the complex and unstructured features of the working environment, the randomness of particle sampling, and the final pose selection problem. In this paper, an improved AMCL algorithm is proposed, aiming to build a laser radar-based robot localization system in a complex and unstructured environment, with a LIDAR point cloud scan-matching process after the particle score calculating process. The weighted mean pose of AMCL particle swarm is used as the initial pose of the scan matching process. The LIDAR point cloud is matched with the probability grid map from coarse to fine using the Gaussian-Newton method, which results in more accurate poses. Moreover, the scan-matching pose is added into the particle swarm as a high-weight particle. So the particle swarm after resampling will be more concentrated in the correct position. The particle filter and the scan-matching process form a closed loop, thus enhancing the localization accuracy of mobile robots. The experiment results demonstrate that the proposed improved AMCL algorithm is superior to the traditional AMCL algorithm in the complex and unstructured environment, by exploiting the high-accuracy characteristic of scan matching while inheriting the stability of AMCL.

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Model based Kalman Filter Mobile Robot Self-Localization

Cited by 10 publications

References 18 publications

On the position accuracy of mobile robot localization based on particle filters combined with scan matching

On the position accuracy of mobile robot localization based on particle filters combined with scan matching

Validation of swarms of robots: theory and experimental results

An Improved AMCL Algorithm Based on Laser Scanning Match in a Complex and Unstructured Environment

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