State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds
There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/7/1614 |
| _version_ | 1798038522359185408 |
|---|---|
| author | Amirali Khodadadian Gostar Chunyun Fu Weiqin Chuah Mohammed Imran Hossain Ruwan Tennakoon Alireza Bab-Hadiashar Reza Hoseinnezhad |
| author_facet | Amirali Khodadadian Gostar Chunyun Fu Weiqin Chuah Mohammed Imran Hossain Ruwan Tennakoon Alireza Bab-Hadiashar Reza Hoseinnezhad |
| author_sort | Amirali Khodadadian Gostar |
| collection | DOAJ |
| description | There is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are commonly some point features or detections collected by the sensor(s) on board the autonomous vehicle. With the increasing utilization of scanners with common autonomous cars, and availability of 3D point clouds in real-time and at fast rates, it is now possible to use more sophisticated features extracted from the point clouds for filtering. This paper presents the idea of using planar features with multi-object Bayesian filters for SLAM. With Bayesian filters, the first step is prediction, where the object states are propagated to the next time based on a stochastic transition model. We first present how such a transition model can be developed, and then propose a solution for state prediction. In the simulation studies, using a dataset of measurements acquired from real vehicle sensors, we apply the proposed model to predict the next planar features and vehicle states. The results show reasonable accuracy and efficiency for statistical filtering-based SLAM applications. |
| first_indexed | 2024-04-11T21:41:21Z |
| format | Article |
| id | doaj.art-b2d6127e00554f51adc153a20400430a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:41:21Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b2d6127e00554f51adc153a20400430a2022-12-22T04:01:35ZengMDPI AGSensors1424-82202019-04-01197161410.3390/s19071614s19071614State Transition for Statistical SLAM Using Planar Features in 3D Point CloudsAmirali Khodadadian Gostar0Chunyun Fu1Weiqin Chuah2Mohammed Imran Hossain3Ruwan Tennakoon4Alireza Bab-Hadiashar5Reza Hoseinnezhad6School of Engineering, RMIT University, Melbourne VIC 3001, AustraliaState Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Engineering, RMIT University, Melbourne VIC 3001, AustraliaSchool of Engineering, RMIT University, Melbourne VIC 3001, AustraliaSchool of Engineering, RMIT University, Melbourne VIC 3001, AustraliaSchool of Engineering, RMIT University, Melbourne VIC 3001, AustraliaSchool of Engineering, RMIT University, Melbourne VIC 3001, AustraliaThere is a large body of literature on solving the SLAM problem for various autonomous vehicle applications. A substantial part of the solutions is formulated based on using statistical (mainly Bayesian) filters such as Kalman filter and its extended version. In such solutions, the measurements are commonly some point features or detections collected by the sensor(s) on board the autonomous vehicle. With the increasing utilization of scanners with common autonomous cars, and availability of 3D point clouds in real-time and at fast rates, it is now possible to use more sophisticated features extracted from the point clouds for filtering. This paper presents the idea of using planar features with multi-object Bayesian filters for SLAM. With Bayesian filters, the first step is prediction, where the object states are propagated to the next time based on a stochastic transition model. We first present how such a transition model can be developed, and then propose a solution for state prediction. In the simulation studies, using a dataset of measurements acquired from real vehicle sensors, we apply the proposed model to predict the next planar features and vehicle states. The results show reasonable accuracy and efficiency for statistical filtering-based SLAM applications.https://www.mdpi.com/1424-8220/19/7/1614simultaneous localization and mappingplanar featuresplane parameterstransition modelBayesian filters |
| spellingShingle | Amirali Khodadadian Gostar Chunyun Fu Weiqin Chuah Mohammed Imran Hossain Ruwan Tennakoon Alireza Bab-Hadiashar Reza Hoseinnezhad State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds Sensors simultaneous localization and mapping planar features plane parameters transition model Bayesian filters |
| title | State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds |
| title_full | State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds |
| title_fullStr | State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds |
| title_full_unstemmed | State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds |
| title_short | State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds |
| title_sort | state transition for statistical slam using planar features in 3d point clouds |
| topic | simultaneous localization and mapping planar features plane parameters transition model Bayesian filters |
| url | https://www.mdpi.com/1424-8220/19/7/1614 |
| work_keys_str_mv | AT amiralikhodadadiangostar statetransitionforstatisticalslamusingplanarfeaturesin3dpointclouds AT chunyunfu statetransitionforstatisticalslamusingplanarfeaturesin3dpointclouds AT weiqinchuah statetransitionforstatisticalslamusingplanarfeaturesin3dpointclouds AT mohammedimranhossain statetransitionforstatisticalslamusingplanarfeaturesin3dpointclouds AT ruwantennakoon statetransitionforstatisticalslamusingplanarfeaturesin3dpointclouds AT alirezababhadiashar statetransitionforstatisticalslamusingplanarfeaturesin3dpointclouds AT rezahoseinnezhad statetransitionforstatisticalslamusingplanarfeaturesin3dpointclouds |