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is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A robust baro-radar-inertial odometry m-estimator for multicopter navigation in cities and forests </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&amp;query=Girod%2C+R">Rik Girod</a>, <a href="/search/cs?searchtype=author&amp;query=Hauswirth%2C+M">Marco Hauswirth</a>, <a href="/search/cs?searchtype=author&amp;query=Pfreundschuh%2C+P">Patrick Pfreundschuh</a>, <a href="/search/cs?searchtype=author&amp;query=Biasio%2C+M">Mariano Biasio</a>, <a href="/search/cs?searchtype=author&amp;query=Siegwart%2C+R">Roland Siegwart</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.05764v1-abstract-short" style="display: inline;"> Search and rescue operations require mobile robots to navigate unstructured indoor and outdoor environments. In particular, actively stabilized multirotor drones need precise movement data to balance and avoid obstacles. Combining radial velocities from on-chip radar with MEMS inertial sensing has proven to provide robust, lightweight, and consistent state estimation, even in visually or geometric&hellip; <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05764v1-abstract-full').style.display = 'inline'; document.getElementById('2408.05764v1-abstract-short').style.display = 'none';">&#9661; More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.05764v1-abstract-full" style="display: none;"> Search and rescue operations require mobile robots to navigate unstructured indoor and outdoor environments. In particular, actively stabilized multirotor drones need precise movement data to balance and avoid obstacles. Combining radial velocities from on-chip radar with MEMS inertial sensing has proven to provide robust, lightweight, and consistent state estimation, even in visually or geometrically degraded environments. Statistical tests robustify these estimators against radar outliers. However, available work with binary outlier filters lacks adaptability to various hardware setups and environments. Other work has predominantly been tested in handheld static environments or automotive contexts. This work introduces a robust baro-radar-inertial odometry (BRIO) m-estimator for quadcopter flights in typical GNSS-denied scenarios. Extensive real-world closed-loop flights in cities and forests demonstrate robustness to moving objects and ghost targets, maintaining a consistent performance with 0.5 % to 3.2 % drift per distance traveled. Benchmarks on public datasets validate the system&#39;s generalizability. The code, dataset, and video are available at https://github.com/ethz-asl/rio. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05764v1-abstract-full').style.display = 'none'; document.getElementById('2408.05764v1-abstract-short').style.display = 'inline';">&#9651; Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication at IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) 2024</span> </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <span class="help" style="display: inline-block;"><a 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