Estimation of the NASA Mars2020 Perseverance rover path through Visual Odometry Article Swipe
Simone Andolfo
,
Flavio Petricca
,
Antonio Genova
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu22-7695
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu22-7695
<p>The future space exploration missions will require autonomous robotic systems capable to safely move across the operational environment and reach sites of scientific interest with limited commands from the ground operators.</p><p>The NASA Mars2020 Perseverance rover is the most advanced robotic vehicle ever sent on the planet Mars and is currently exploring the Jezero crater searching for signs of ancient life and investigating the geological history of the planet. The increased computational resources of the Perseverance’s onboard computer enable the navigation software to continuously adjust the path, by processing visual inputs through the navigation cameras. The stereo images with the left and right rover cameras are analyzed to build local 3D maps of the surrounding terrain to identify hazardous areas (<em>e.g.</em>, steep slopes) that could affect the rover’s safety.</p><p>We use Visual Odometry (VO) methods to accurately update the rover’s position and attitude (<em>i.e.</em>, pose), by detecting and tracking the image-locations of landmarks (<em>e.g.</em>, the sharp edge of a rock) through successive stereo pairs. VO is a fundamental technique to enhance the localization accuracies of wheeled vehicles in planetary environments where Global Navigation Satellite Systems (GNSS) are not available.</p><p>We present here the reconstructed position and attitude of the Perseverance rover that we retrieved by processing images acquired by the navigation cameras during sols 65, 66, 72, and 120. 3D-to-3D algorithms were applied accounting for the nonlinear optical effects that affect the raw images. The estimated rover’s orientation is fully in line with the accurate measurements provided by the onboard Inertial Measurement Units (IMUs). The displacements between the telemetered and the reconstructed rover’s location suggest errors in the WO measurements, which are compensated by our VO estimate.</p>
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-egu22-7695
- OA Status
- gold
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- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4220870934Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-egu22-7695Digital Object Identifier
- Title
-
Estimation of the NASA Mars2020 Perseverance rover path through Visual OdometryWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-27Full publication date if available
- Authors
-
Simone Andolfo, Flavio Petricca, Antonio GenovaList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-egu22-7695Publisher landing page
- Open access
-
YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/egusphere-egu22-7695Direct OA link when available
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Visual odometry, Artificial intelligence, Computer science, Odometry, Path integration, Path (computing), Computer vision, Mars Exploration Program, Robot, Mobile robot, Real-time computing, Astrobiology, Biology, Programming languageTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.present | 187 |
| abstract_inverted_index.require | 6 |
| abstract_inverted_index.robotic | 8, 39 |
| abstract_inverted_index.slopes) | 121 |
| abstract_inverted_index.suggest | 261 |
| abstract_inverted_index.systems | 9 |
| abstract_inverted_index.terrain | 114 |
| abstract_inverted_index.through | 90, 158 |
| abstract_inverted_index.vehicle | 40 |
| abstract_inverted_index.wheeled | 173 |
| abstract_inverted_index.3D-to-3D | 216 |
| abstract_inverted_index.Inertial | 247 |
| abstract_inverted_index.Mars2020 | 32 |
| abstract_inverted_index.Odometry | 130 |
| abstract_inverted_index.accurate | 241 |
| abstract_inverted_index.acquired | 204 |
| abstract_inverted_index.advanced | 38 |
| abstract_inverted_index.analyzed | 105 |
| abstract_inverted_index.attitude | 140, 193 |
| abstract_inverted_index.cameras. | 93 |
| abstract_inverted_index.commands | 26 |
| abstract_inverted_index.computer | 76 |
| abstract_inverted_index.identify | 116 |
| abstract_inverted_index.interest | 23 |
| abstract_inverted_index.location | 260 |
| abstract_inverted_index.missions | 4 |
| abstract_inverted_index.position | 138, 191 |
| abstract_inverted_index.provided | 243 |
| abstract_inverted_index.software | 80 |
| abstract_inverted_index.tracking | 146 |
| abstract_inverted_index.vehicles | 174 |
| abstract_inverted_index.Satellite | 181 |
| abstract_inverted_index.currently | 49 |
| abstract_inverted_index.detecting | 144 |
| abstract_inverted_index.estimated | 232 |
| abstract_inverted_index.exploring | 50 |
| abstract_inverted_index.hazardous | 117 |
| abstract_inverted_index.increased | 69 |
| abstract_inverted_index.landmarks | 150 |
| abstract_inverted_index.nonlinear | 223 |
| abstract_inverted_index.planetary | 176 |
| abstract_inverted_index.resources | 71 |
| abstract_inverted_index.retrieved | 200 |
| abstract_inverted_index.searching | 54 |
| abstract_inverted_index.technique | 166 |
| abstract_inverted_index.Navigation | 180 |
| abstract_inverted_index.accounting | 220 |
| abstract_inverted_index.accuracies | 171 |
| abstract_inverted_index.accurately | 134 |
| abstract_inverted_index.algorithms | 217 |
| abstract_inverted_index.autonomous | 7 |
| abstract_inverted_index.geological | 63 |
| abstract_inverted_index.navigation | 79, 92, 207 |
| abstract_inverted_index.processing | 87, 202 |
| abstract_inverted_index.scientific | 22 |
| abstract_inverted_index.successive | 159 |
| abstract_inverted_index.Measurement | 248 |
| abstract_inverted_index.compensated | 269 |
| abstract_inverted_index.environment | 17 |
| abstract_inverted_index.exploration | 3 |
| abstract_inverted_index.fundamental | 165 |
| abstract_inverted_index.operational | 16 |
| abstract_inverted_index.orientation | 234 |
| abstract_inverted_index.surrounding | 113 |
| abstract_inverted_index.telemetered | 255 |
| abstract_inverted_index.Perseverance | 33, 196 |
| abstract_inverted_index.continuously | 82 |
| abstract_inverted_index.environments | 177 |
| abstract_inverted_index.localization | 170 |
| abstract_inverted_index.measurements | 242 |
| abstract_inverted_index.computational | 70 |
| abstract_inverted_index.displacements | 252 |
| abstract_inverted_index.investigating | 61 |
| abstract_inverted_index.measurements, | 266 |
| abstract_inverted_index.reconstructed | 190, 258 |
| abstract_inverted_index.image-locations | 148 |
| abstract_inverted_index.&lt;p&gt;The | 0 |
| abstract_inverted_index.rover&amp;#8217;s | 126, 137, 233, 259 |
| abstract_inverted_index.estimate.&lt;/p&gt; | 273 |
| abstract_inverted_index.Perseverance&amp;#8217;s | 74 |
| abstract_inverted_index.(&lt;em&gt;e.g.&lt;/em&gt;, | 119, 151 |
| abstract_inverted_index.(&lt;em&gt;i.e.&lt;/em&gt;, | 141 |
| abstract_inverted_index.safety.&lt;/p&gt;&lt;p&gt;We | 127 |
| abstract_inverted_index.available.&lt;/p&gt;&lt;p&gt;We | 186 |
| abstract_inverted_index.operators.&lt;/p&gt;&lt;p&gt;The | 30 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5010404501, https://openalex.org/A5067068327, https://openalex.org/A5085319831 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I861853513 |
| citation_normalized_percentile.value | 0.02592554 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |