Optimization techniques for laser-based 3D particle filter SLAM

Welle, Jochen; Schulz, Dirk; Bachran, Thomas; Cremers, Armin B.

doi:10.1109/robot.2010.5509992

Cited by 16 publications

(11 citation statements)

References 5 publications

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“…Particle filter is a method based on Monte Carlo [8], [9] and recursive Bayesian sequential estimation [19], [20] that is very suitable for nonlinear and/or non-Gaussian applications [21]- [23].…”

Section: Theory Of Particle Filtermentioning

confidence: 99%

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Bernardis

Vicente-Guijalba

Martínez-Marín

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Information of crop phenology is essential for evaluating crop productivity. In a previous work, we determined phenological stages with remote sensing data using a dynamic system framework and an extended Kalman filter (EKF) approach. In this paper, we demonstrate that the particle filter is a more reliable method to infer any phenological stage compared to the EKF. The improvements achieved with this approach are discussed. In addition, this methodology enables the estimation of key cultivation dates, thus providing a practical product for many applications. The dates of some important stages, as the sowing date and the day when the crop reaches the panicle initiation stage, have been chosen to show the potential of this technique.

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Section: Theory Of Particle Filtermentioning

confidence: 99%

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Bernardis

Vicente-Guijalba

Martínez-Marín

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, their goal is to answer event queries instead of spatial queries, which is different from the goal of this research. Furthermore, a hot research topic of the robotics research community, simultaneous localization and mapping (SLAM), also makes extensive utilization of particle filters [27,2].…”

Section: Rfid-based Track and Tracementioning

confidence: 99%

An RFID and particle filter-based indoor spatial query evaluation system

Sun

et al. 2013

Proceedings of the 16th International Conference on Extending Database Technology

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People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various locationbased applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because particle filters can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the particle filter-based location inference method as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through extensive simulations with real-world parameters. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently.

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“…The robot might plan its path to go through the desk area and collide with the desk, since there is nothing in this area except these four points in the 2D map. Therefore, 3D mapping methods based on multiple laser sensors or 3D distance sensors have been noticed [6][7][8]. 3D mapping methods can reflect the shapes of objects in the environment correctly and the robot can plan its path without any collision, but the cost is usually high since view angle of 3D sensors is limited and setting multiple sensors will increase the cost greatly.…”

Section: Introductionmentioning

confidence: 99%

Robust 2D Mapping Integrating with 3D Information for the Autonomous Mobile Robot Under Dynamic Environment

2019

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In order to move around automatically, mobile robots usually need to recognize their working environment first. Simultaneous localization and mapping (SLAM) has become an important research field recently, by which the robot can generate a map while moving around. Both two-dimensional (2D) mapping and three-dimensional (3D) mapping methods have been developed greatly with high accuracy. However, 2D maps cannot reflect the space information of the environment and 3D mapping needs long processing time. Moreover, conventional SLAM methods based on grid maps take a long time to delete the moving objects from the map and are hard to delete the potential moving objects. In this paper, a 2D mapping method integrating with 3D information based on immobile area occupied grid maps is proposed. Objects in 3D space are recognized and their space information (e.g., shapes) and properties (moving objects or potential moving objects like people standing still) are projected to the 2D plane for updating the 2D map. By using the immobile area occupied grid map method, recognized still objects are reflected to the map quickly by updating the immobile area occupancy probability with a high coefficient. Meanwhile, recognized moving objects and potential moving objects are not used for updating the map. The unknown objects are reflected to the 2D map with a lower immobile area occupancy probability so that they can be deleted quickly once they are recognized as moving objects or start to move. The effectiveness of our method is proven by experiments of mapping under dynamic indoor environment using a mobile robot.

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Optimization techniques for laser-based 3D particle filter SLAM

Cited by 16 publications

References 5 publications

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

An RFID and particle filter-based indoor spatial query evaluation system

Robust 2D Mapping Integrating with 3D Information for the Autonomous Mobile Robot Under Dynamic Environment

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