Error modeling of Laser Range Finder for robotic application using time domain technique

Jain, Shikha; Nandy, Sambhunath; Chakraborty, Goutam; Kumar, C. S.; Ray, R.K.; Shome, Sankar Nath

doi:10.1109/icspcc.2011.6061771

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…The most studied scanner is the Sick LMS-200, which was first characterized in [4] and then further investigated in [5], [6], [7], [8], and [9]. A new generation of smaller Sick laser (LMS-100 family) was evaluated in [10].…”

Section: Related Workmentioning

confidence: 99%

Noise characterization of depth sensors for surface inspections

Pomerleau¹,

Breitenmoser²,

Liu³

et al. 2012

2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI)

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International audienceIn the context of environment reconstruction for inspection, it is important to handle sensor noise properly to avoid distorted representations. A short survey of available sensors is realize to help their selection based on the payload capability of a robot. We then propose uncertainty models based on empirical results for three models of laser rangefinders: Hokuyo URG-04LX, UTM-30LX and the Sick LMS-151. The methodology, used to characterize those sensors, targets more specifically different metallic materials which often give distorted images due to reflexion. We also evaluate the impact of sensor noise on surface normal vector reconstruction and conclude with observations about the impact of sunlight and reflexions

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Section: Related Workmentioning

confidence: 99%

Noise characterization of depth sensors for surface inspections

Pomerleau¹,

Breitenmoser²,

Liu³

et al. 2012

2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI)

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“…Integrated with kinematics, dynamics, and laser sensor, the robot in the simulation platform can also be controlled manually through a joystick for driver training (manual mode). In this simulation platform [32], the model of LRF (SICK LMS111) is embedded, the flicker noise and white noise [33] are fused in the laser-scan observation, and the error model of the odometer is also built. The ground truth of the robot pose could be outputted directly to be compared with the experimental result.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Multilayer Matching SLAM for Large-Scale and Spacious Environments

Wang

Liu

et al. 2015

International Journal of Advanced Robotic Systems

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In large-scale and spacious environments, keeping a reliable data association and reducing computational complexity are challenges for the implementation of Simultaneous Localization and Mapping (SLAM). Focused on these problems, a multilayer-matching-based incremental SLAM algorithm is proposed in this article. In this algorithm, SLAM is simplified as a problem composed of a least-square-based optimization problem and data association. Then, it is solved in two steps. Firstly, a multilayer matching method is applied to deal with the data-association problem. Both matching between observation and local map and matching between different local maps are carried out. The uncertainty of the results-matching is described by the Fisher information matrix. Secondly, the robot pose is optimized through an incremental QR decomposition method. This algorithm effectively avoids the local minima caused by the limited observation information, and can build a consistent map of the environment. Meanwhile, the characters (hierarchical and incremental) of the proposed algorithm ensure low computational complexity. Experiments on simulation environments and two kinds of real environments with different sparse features verify that the algorithm is applicable for real-time application in large-scale and spacious environments.

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“…Simulation 2: In Simulation 2, the range finder sensor is considered as a LRF and the noise model proposed in ref. [16] was used to simulate the LRF noise. The modeled noise is a combination of flicker noise and white noise with standard deviation of 1.77023 mm and 3.03856 mm (see Fig.…”

Section: Computer Simulationsmentioning

confidence: 99%

A safe area search and map building algorithm for a wheeled mobile robot in complex unknown cluttered environments

Savkin

2017

Robotica

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SUMMARYIn this paper, a safe map building and area search algorithm for a mobile robot in a closed unknown environment with obstacles is presented. A range finder sensor is used to detect the environment. The objective is to perform a complete search of the environment and build a complete map of it while avoiding collision with the obstacles. The developed robot navigation algorithm is randomized. It is proved that with probability 1 the robot completes its task in a finite time. Computer simulations and experiments with a real Pioneer-3DX robot confirm the performance of the proposed method.

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Error modeling of Laser Range Finder for robotic application using time domain technique

Cited by 8 publications

References 16 publications

Noise characterization of depth sensors for surface inspections

Noise characterization of depth sensors for surface inspections

Multilayer Matching SLAM for Large-Scale and Spacious Environments

A safe area search and map building algorithm for a wheeled mobile robot in complex unknown cluttered environments

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