SLAM-R Algorithm of Simultaneous Localization and Mapping Using RFID for Obstacle Location and Recognition

Lemus, Ricardo Guerrero; Díaz, Sergio; Gutierrez, Citlalih; Rodríguez, D.; Escobar, F.

doi:10.1016/s1665-6423(14)71634-7

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…Contextual features in the environment have been used for a variety of purposes, including: (1) localization [36][37][38], (2) map annotation [21,22,[39][40][41][42][43], (3) SLAM using text [44,45], and (4) Semantic SLAM [32,[46][47][48][49].…”

Section: Mapping and Localization Using Contextual Informationmentioning

confidence: 99%

“…Context, and feature-annotated maps have been used for SLAM purposes such as in [21,22,[39][40][41][42][43]. In [39], a method for combining and correcting occupancy grids using feature measurements was presented.…”

Section: Map Annotationmentioning

confidence: 99%

“…The features (e.g., object locations) were used for localization and to partition the environment into triangular regions, with local occupancy grids defined relative to these features. In [40], a modified tinySLAM method was presented. The tinySLAM method fused laser data, odometry, and RFID tags detected in the environment to create an occupancy grid.…”

Section: Map Annotationmentioning

confidence: 99%

“…If features are occluded, error accumulation occurs, resulting in an inconsistent map. Furthermore, some methods require the environment to be modified with artificial landmarks (i.e., April or RFID tags) [36,40], or require unique landmarks [38,44]. Lastly, it is infeasible to create an object detector for all items within a grocery store environment, as the number of classes required for a generic classifier may be on the order of 10 5 [50,51].…”

Section: Semantic Slammentioning

confidence: 99%

See 3 more Smart Citations

A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments

et al. 2021

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Grocery shoppers must negotiate cluttered, crowded, and complex store layouts containing a vast variety of products to make their intended purchases. This complexity may prevent even experienced shoppers from finding their grocery items, consuming a lot of their time and resulting in monetary loss for the store. To address these issues, we present a generic grocery robot architecture for the autonomous search and localization of products in crowded dynamic unknown grocery store environments using a unique context Simultaneous Localization and Mapping (contextSLAM) method. The contextSLAM method uniquely creates contextually rich maps through the online fusion of optical character recognition and occupancy grid information to locate products and aid in robot localization in an environment. The novelty of our robot architecture is in its ability to intelligently use geometric and contextual information within the context map to direct robot exploration in order to localize products in unknown environments in the presence of dynamic people. Extensive experiments were conducted with a mobile robot to validate the overall architecture and contextSLAM, including in a real grocery store. The results of the experiments showed that our architecture was capable of searching for and localizing all products in various grocery lists in different unknown environments.

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Section: Mapping and Localization Using Contextual Informationmentioning

confidence: 99%

Section: Map Annotationmentioning

confidence: 99%

Section: Map Annotationmentioning

confidence: 99%

Section: Semantic Slammentioning

confidence: 99%

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A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments

et al. 2021

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“…Combining data from RFIDs, odometer and IMU has been found effective to reduce IMU drift incurred for localisation and mapping [26]. A few studies have targeted avoiding using inertial sensors altogether and focused solely on using a laser scanner and RFIDs for accurate localisation and mapping [24,27,28]. Inconsistencies in laser frame stitching were observed when separations between installed RFID tags were large.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Unique-Identifier-Based Automated Georeferencing and Coregistration of Point Clouds in Underground Mines

2021

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Spatially referenced and geometrically accurate laser scans are essential for mapping and monitoring applications in underground mines to ensure safe and smooth operation. However, obtaining an absolute 3D map in an underground mine environment is challenging using laser scanning due to the unavailability of global navigation satellite system (GNSS) signals. Consequently, applications that require georeferenced point cloud or coregistered multitemporal point clouds such as detecting changes, monitoring deformations, tracking mine logistics, measuring roadway convergence rate and evaluating construction performance become challenging. Current mapping practices largely include a manual selection of discernable reference points in laser scans for georeferencing and coregistration which is often time-consuming, arduous and error-prone. Moreover, challenges in obtaining a sensor positioning framework, the presence of structurally symmetric layouts and highly repetitive features (such as roof bolts) makes the multitemporal scans difficult to georeference and coregister. This study aims at overcoming these practical challenges through development of three-dimensional unique identifiers (3DUIDs) and a 3D registration (3DReG) workflow. Field testing of the developed approach in an underground coal mine has been found effective with an accuracy of 1.76 m in georeferencing and 0.16 m in coregistration for a scan length of 850 m. Additionally, automatic extraction of mine roadway profile has been demonstrated using 3DUID which is often a compliant and operational requirement for mitigating roadway related hazards that includes roadway convergence rate, roof/rock falls, floor heaves and vehicle clearance for collision avoidance. Potential applications of 3DUID include roadway profile extraction, guided automation, sensor calibration, reference targets for a routine survey and deformation monitoring.

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Particle filter based landmark mapping for SLAM of mobile robot based on RFID system

Wang

Takahashi

2016

2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

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This paper proposes a novel Simultaneous Localization and Mapping (SLAM) based on distributed particle updates for landmark mapping and validates it with an HFband RFID-based mobile robot. Multiple RFID readers are embedded at the bottom of an omni-directional vehicle and tags are installed on the floor. The IC tags are used as landmarks of the environment. FastSLAM[1] uses particles to estimate the position and orientation of the robot and Kalman filter to update the positions of IC tags. However, an update of the detected IC tags with Kalman filter is not appropriate because the probability of the IC tag detection cannot be modeled with a Gaussian distribution. We use two separate particle filters to estimate both the position and orientation of the robot and positions of IC tags simultaneously. The proposed method has been tested on the simulated and real environments. Experimental results show the validity and computational efficiency of the proposed method.

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SLAM-R Algorithm of Simultaneous Localization and Mapping Using RFID for Obstacle Location and Recognition

Cited by 9 publications

References 2 publications

A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments

A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments

Three-Dimensional Unique-Identifier-Based Automated Georeferencing and Coregistration of Point Clouds in Underground Mines

Particle filter based landmark mapping for SLAM of mobile robot based on RFID system

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