Method to estimate angular velocity from accelerometers during gyroscope saturation in SLAM reduces median localization error by 71.5% translation and 65.5% rotation, with new TIGS dataset of high-angular-velocity motions.
Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge,
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DeepDetect trains ESPNet on fused classical detector masks to produce dense, repeatable keypoints that outperform prior methods on Oxford, HPatches, and Middlebury benchmarks.
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Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions
Method to estimate angular velocity from accelerometers during gyroscope saturation in SLAM reduces median localization error by 71.5% translation and 65.5% rotation, with new TIGS dataset of high-angular-velocity motions.
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DeepDetect: Learning All-in-One Dense Keypoints
DeepDetect trains ESPNet on fused classical detector masks to produce dense, repeatable keypoints that outperform prior methods on Oxford, HPatches, and Middlebury benchmarks.