連続時間キャリブレーションとUWB・SLAM観測の融合による座標一貫性のある自己位置推定
Coordinate-Consistent Localization via Continuous-Time Calibration and Fusion of UWB and SLAM Observations
UWBとSLAMのデータを2段階でキャリブレーション・融合し、同一環境で座標系が一貫した高精度な自己位置推定を実現する手法を提案した論文。
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著者: Tien-Dat Nguyen, Thien-Minh Nguyen, Vinh-Hao Nguyen
分類: cs.RO
原文アブストラクト
Onboard simultaneous localization and mapping (SLAM) methods are commonly used to provide accurate localization information for autonomous robots. However, the coordinate origin of SLAM estimate often resets for each run. On the other hand, UWB-based localization with fixed anchors can ensure a consistent coordinate reference across sessions; however, it requires an accurate assignment of the anchor nodes' coordinates. To this end, we propose a two-stage approach that calibrates and fuses UWB data and SLAM data to achieve coordinate-wise consistent and accurate localization in the same environment. In the first stage, we solve a continuous-time batch optimization problem by using the range and odometry data from one full run, incorporating height priors and anchor-to-anchor distance factors to recover the anchors' 3D positions. For the subsequent runs in the second stage, a sliding-window optimization scheme fuses the UWB and SLAM data, which facilitates accurate localization in the same coordinate system. Experiments are carried out on the NTU VIRAL dataset with six scenarios of UAV flight, and we show that calibration using data in one run is sufficient to enable accurate localization in the remaining runs. We release our source code to benefit the community at https://github.com/ntdathp/slam-uwb-calibration.
