Outdoor robot navigation using Gmapping based SLAM algorithm

Balasuriya, B.L.E.A.; Chathuranga, Bhagya; Jayasundara, B.H.M.D.; Napagoda, N.R.A.C.; Kumarawadu, Sisil; Chandima, D. P.; Jayasekara, A. G. Buddhika P.

doi:10.1109/mercon.2016.7480175

Cited by 60 publications

(23 citation statements)

References 7 publications

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“…The combined usage of

and DWA allows the robot to move from one point to another without a collision. The position of the robot in the global co-ordinate is provided by the G-Mapping SLAM algorithm [ 40 ]. Figure 7 shows the process flow of the probable dirt region identification using semantic segmentation and periodic pattern filter [ 23 ].…”

Section: Exploration Strategymentioning

confidence: 99%

An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning

Pathmakumar

Kalimuthu

Elara

et al. 2021

Sensors

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Cleaning is an important factor in most aspects of our day-to-day life. This research work brings a solution to the fundamental question of “How clean is clean” by introducing a novel framework for auditing the cleanliness of built infrastructure using mobile robots. The proposed system presents a strategy for assessing the quality of cleaning in a given area and a novel exploration strategy that facilitates the auditing in a given location by a mobile robot. An audit sensor that works by the “touch and inspect” analogy that assigns an audit score corresponds to its area of inspection has been developed. A vision-based dirt-probability-driven exploration is proposed to empower a mobile robot with an audit sensor on-board to perform auditing tasks effectively. The quality of cleaning is quantified using a dirt density map representing location-wise audit scores, dirt distribution pattern obtained by kernel density estimation, and cleaning benchmark score representing the extent of cleanliness. The framework is realized in an in-house developed audit robot to perform the cleaning audit in indoor and semi-outdoor environments. The proposed method is validated by experiment trials to estimate the cleanliness in five different locations using the developed audit sensor and dirt-probability-driven exploration.

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“…The combined usage of

Section: Exploration Strategymentioning

confidence: 99%

An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning

Pathmakumar

Kalimuthu

Elara

et al. 2021

Sensors

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“…SLAM involves two types of devices: one is 2D SLAM using Flash LIDAR F4 and the other is 3D SLAM using an Intel RealSense Depth Camera D435i. The 2D SLAM in the system is mainly based on Robot Operating System (ROS) [42] and GMmapping algorithm [43,44], which is a more reliable and mature algorithm based on LIDAR and odometer solutions. The core of the GMapping algorithm is a particle filter, which helps to calculate the distribution of variables by combining samples with parameter density functions.…”

Section: B Slam and Image Recognitionmentioning

confidence: 99%

A Smart Sterilization Robot System With Chlorine Dioxide for Spray Disinfection

et al. 2021

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The highly infectious and serious nature of coronavirus disease 2019 (COVID-19) has highlighted the need for hospital space disinfection technology and the prevention of human exposure to pathogenic environments. This research developed novel chlorine dioxide (ClO2) sterilization technology to reduce bacteria and viruses in the air and on surfaces. A smart sterilization robot system was also developed to spray disinfectants in operating theaters or patients' rooms, designed according to the results of controlled experiments and the requirements for hospital disinfection. The system was built incorporated a semi-automatic remote-controlled module and an automatic intelligent disinfection function; that is, it could operate independently according to specific epidemic prevention strategies, which were implemented using a combination of Internet of Things (IoT) applications and a gesture recognition function. The elimination of Escherichia coli (E. coli) bacteria on sample plates was 99.8 % effective. This paper reviews the evolution of various disinfection technologies and describes a disinfection robot system in detail.

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“…That means that a high precision GNSS receiver is needed, but only for the map generation. The Laser information is transformed accordingly to the GNSS position at each time step and then accumulated into the map using gmapping Simultaneous Localization and Mapping (SLAM) method [25]. The generated map (Figure 2) has all the features needed later by the AMCL algorithm to match a new laser scan with it.…”

Section: Map Generationmentioning

confidence: 99%

Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

Miguel

García

Armingol

2020

Sensors

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This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results.

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Outdoor robot navigation using Gmapping based SLAM algorithm

Cited by 60 publications

References 7 publications

An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning

An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning

A Smart Sterilization Robot System With Chlorine Dioxide for Spray Disinfection

Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

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