Particle filter robot localisation through robust fusion of laser, WiFi, compass, and a network of external cameras

Canedo-Rodriguez, A.; Alvarez-Santos, V.; Regueiro, Carlos V.; Iglesias, Roberto; Barro, Senén; Presedo, J.

doi:10.1016/j.inffus.2015.03.006

Cited by 43 publications

(21 citation statements)

References 62 publications

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“…Durrant-Whyte and Bailey [52] reported the SLAM solutions based on extended Kalman filter (EKF) and PF using vision system data. Canedo-Rodríguez et al [31] argued that the SLAM based on vision system alone usually fails when there is not enough geometric variation (such as indoors) and when people are walking around as they cause occlusions. They proposed a PF-based sensor fusion method from data generated using multimodal sensors such as laser rangefinder, Wi-Fi, cameras and magnetic compass to overcome the limitation.…”

Section: Localisation Mapping and Tracking In Hrcmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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Section: Localisation Mapping and Tracking In Hrcmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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“…Another multi-sensor particle filter fusion based algorithm for mobile robot localization was proposed by [14]. The algorithm was able to fuse data coming from various types of sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Authors also proposed an easy and fast deployment mechanism of the proposed system. A laser range-finder, a WiFi system, many external cameras, and a magnetic compass along with a probabilistic and mapping strategy were used to validate the work proposed [14]. …”

Section: Related Workmentioning

confidence: 99%

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

Almasri

Elleithy

Alajlan

2015

Sensors

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Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.

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“…So researchers are committed to combine odometry with external sensors to eliminate the odomerty errors. External sensors, such as cameras, [14][15][16][17][18][19] laser range finder, 15,16 ultrasonic, 20 radio frequency identification devices (RFID), 21 and electronic compass, 16,19 have been used to obtain the required measurements.…”

Section: Introductionmentioning

confidence: 99%

An indoor mobile robot navigation technique using odometry and electronic compass

Chen,

Zhang

2017

International Journal of Advanced Robotic Systems

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A novel method of indoor mobile robot navigation is presented. The proposed approach fuses the data of odometry and electronic compass for navigation. It includes two calibration methods and a fusion algorithm. First of all, calibration method of systematic odometry error is used to reduce the error of navigation and provide the reliable estimate of pose of mobile robot for adaptive extended Kalman filter fusion algorithm later. Secondly, calibration method of electronic compass using an adaptive neural fuzzy inference system provides direction angle of mobile robot as observation for adaptive extended Kalman filter algorithm later. Finally, a fusion algorithm using adaptive extended Kalman filter algorithm fuses data of odometry and electronic compass that provides the position and orientation of mobile robot. In addition, the value of the parameter k of adaptive extended Kalman filter algorithm which is related to the process noise covariance is decided by fuzzy algorithm. In order to illustrate the feasibility of the proposed approach, two types of experiments are done: the first-type experiment is that the mobile robot runs along default path only with odometry, and the mobile robot with odometry and electronic compass in the second-type experiment utilizes the proposed approach for navigation. The results of experiments show that the error of localization and navigation in the first-type experiment is larger than one in the second-type experiment. They prove the good performance of the proposed approach.

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Particle filter robot localisation through robust fusion of laser, WiFi, compass, and a network of external cameras

Cited by 43 publications

References 62 publications

Symbiotic human-robot collaborative assembly

Symbiotic human-robot collaborative assembly

Sensor Fusion Based Model for Collision Free Mobile Robot Navigation

An indoor mobile robot navigation technique using odometry and electronic compass

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