ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter
Event-based vision sensors show great promise for use in embedded applications requiring low-latency passive sensing at a low computational cost. In this paper, we present an event-based algorithm that relies on an Extended Kalman Filter for 6-Degree of Freedom sensor pose estimation. The algorithm...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/23/7840 |
| _version_ | 1827674679883595776 |
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| author | Fabien Colonnier Luca Della Vedova Garrick Orchard |
| author_facet | Fabien Colonnier Luca Della Vedova Garrick Orchard |
| author_sort | Fabien Colonnier |
| collection | DOAJ |
| description | Event-based vision sensors show great promise for use in embedded applications requiring low-latency passive sensing at a low computational cost. In this paper, we present an event-based algorithm that relies on an Extended Kalman Filter for 6-Degree of Freedom sensor pose estimation. The algorithm updates the sensor pose event-by-event with low latency (worst case of less than 2 μs on an FPGA). Using a single handheld sensor, we test the algorithm on multiple recordings, ranging from a high contrast printed planar scene to a more natural scene consisting of objects viewed from above. The pose is accurately estimated under rapid motions, up to 2.7 m/s. Thereafter, an extension to multiple sensors is described and tested, highlighting the improved performance of such a setup, as well as the integration with an off-the-shelf mapping algorithm to allow point cloud updates with a 3D scene and enhance the potential applications of this visual odometry solution. |
| first_indexed | 2024-03-10T04:45:42Z |
| format | Article |
| id | doaj.art-b47a0d79e208448887b11f30b50237a0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T04:45:42Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b47a0d79e208448887b11f30b50237a02023-11-23T03:00:05ZengMDPI AGSensors1424-82202021-11-012123784010.3390/s21237840ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman FilterFabien Colonnier0Luca Della Vedova1Garrick Orchard2Temasek Laboratories, National University of Singapore, Singapore 117411, SingaporeOpen Source Robotics Corporation, Singapore 138633, SingaporeTemasek Laboratories, National University of Singapore, Singapore 117411, SingaporeEvent-based vision sensors show great promise for use in embedded applications requiring low-latency passive sensing at a low computational cost. In this paper, we present an event-based algorithm that relies on an Extended Kalman Filter for 6-Degree of Freedom sensor pose estimation. The algorithm updates the sensor pose event-by-event with low latency (worst case of less than 2 μs on an FPGA). Using a single handheld sensor, we test the algorithm on multiple recordings, ranging from a high contrast printed planar scene to a more natural scene consisting of objects viewed from above. The pose is accurately estimated under rapid motions, up to 2.7 m/s. Thereafter, an extension to multiple sensors is described and tested, highlighting the improved performance of such a setup, as well as the integration with an off-the-shelf mapping algorithm to allow point cloud updates with a 3D scene and enhance the potential applications of this visual odometry solution.https://www.mdpi.com/1424-8220/21/23/7840event-based sensorvisual odometryextended Kalman filtercomputer visionstructureless measurement model |
| spellingShingle | Fabien Colonnier Luca Della Vedova Garrick Orchard ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter Sensors event-based sensor visual odometry extended Kalman filter computer vision structureless measurement model |
| title | ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter |
| title_full | ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter |
| title_fullStr | ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter |
| title_full_unstemmed | ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter |
| title_short | ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter |
| title_sort | espee event based sensor pose estimation using an extended kalman filter |
| topic | event-based sensor visual odometry extended Kalman filter computer vision structureless measurement model |
| url | https://www.mdpi.com/1424-8220/21/23/7840 |
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