Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes
It is essential that a robot has the ability to determine its position and orientation to execute tasks autonomously. Heading estimation is especially challenging in indoor environments where magnetic distortions make magnetometer-based heading estimation difficult. Ultra-wideband (UWB) transceivers...
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| Veröffentlicht in: | IEEE robotics and automation letters 2021-10, Vol.6 (4), p.8387-8393 |
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| container_title | IEEE robotics and automation letters |
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| creator | Lisus, Daniil Cossette, Charles Champagne Shalaby, Mohammed Forbes, James Richard |
| description | It is essential that a robot has the ability to determine its position and orientation to execute tasks autonomously. Heading estimation is especially challenging in indoor environments where magnetic distortions make magnetometer-based heading estimation difficult. Ultra-wideband (UWB) transceivers are common in indoor localization problems. This letter experimentally demonstrates how to use UWB range and received signal strength (RSS) measurements to estimate robot heading. The RSS of a UWB antenna varies with its orientation. As such, a Gaussian process (GP) is used to learn a data-driven relationship from UWB range and RSS inputs to orientation outputs. Combined with a gyroscope in an invariant extended Kalman filter, this realizes a heading estimation method that uses only UWB and gyroscope measurements. |
| doi_str_mv | 10.1109/LRA.2021.3102300 |
| format | Article |
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Heading estimation is especially challenging in indoor environments where magnetic distortions make magnetometer-based heading estimation difficult. Ultra-wideband (UWB) transceivers are common in indoor localization problems. This letter experimentally demonstrates how to use UWB range and received signal strength (RSS) measurements to estimate robot heading. The RSS of a UWB antenna varies with its orientation. As such, a Gaussian process (GP) is used to learn a data-driven relationship from UWB range and RSS inputs to orientation outputs. Combined with a gyroscope in an invariant extended Kalman filter, this realizes a heading estimation method that uses only UWB and gyroscope measurements.</description><subject>Dipole antennas</subject><subject>Estimation</subject><subject>Extended Kalman filter</subject><subject>Gaussian process</subject><subject>Gaussian process regression</subject><subject>Gaussian processes</subject><subject>Gyroscopes</subject><subject>Indoor environments</subject><subject>Localization</subject><subject>machine learning</subject><subject>Magnetometers</subject><subject>Orientation</subject><subject>Predictive models</subject><subject>Robots</subject><subject>sensor fusion</subject><subject>Signal processing</subject><subject>Signal strength</subject><subject>Training</subject><subject>Transceivers</subject><subject>Ultrawideband</subject><issn>2377-3766</issn><issn>2377-3766</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkNFLwzAQxoMoOObeBV8CPndekrZpHseYmzBQNsceQ5pcZ8ZsZ9IJ_ve2bIhPd9x93_Hdj5B7BmPGQD0tV5MxB87GggEXAFdkwIWUiZB5fv2vvyWjGPcAwDIuhcoGZLtA43y9o7PY-k_T-qamm9gPNoc2mGTrHZamdnSFFv03Orr2u9oc6LoNWO_aD9ov5-YUozc1fQuNxRgx3pGbyhwiji51SDbPs_fpIlm-zl-mk2ViuWJtUqZoyxxkkapCOpVxEKoElxeVAumkq0omCutSnlpbWjSp5cKayqlcZo47JYbk8Xz3GJqvE8ZW75tT6AJGzTPJOGdcpp0KziobmhgDVvoYum_Dj2age4K6I6h7gvpCsLM8nC0eEf_kKoOiyDPxC6FjbFQ</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Lisus, Daniil</creator><creator>Cossette, Charles Champagne</creator><creator>Shalaby, Mohammed</creator><creator>Forbes, James Richard</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Dipole antennas Estimation Extended Kalman filter Gaussian process Gaussian process regression Gaussian processes Gyroscopes Indoor environments Localization machine learning Magnetometers Orientation Predictive models Robots sensor fusion Signal processing Signal strength Training Transceivers Ultrawideband |
| title | Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes |
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