計算資源制約下の航空ナビゲーション向けレーダー慣性オドメトリ
Radar-Inertial Odometry For Computationally Constrained Aerial Navigation
IMUとレーダーを融合し、低コストな民生センサと組み込み計算機でリアルタイムにUAVのナビゲーション状態を推定するレーダー慣性オドメトリ手法を提案した論文。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Jan Michalczyk
分類: cs.RO
原文アブストラクト
Recently, the progress in the radar sensing technology consisting in the miniaturization of the packages and increase in measuring precision has drawn the interest of the robotics research community. Indeed, a crucial task enabling autonomy in robotics is to precisely determine the pose of the robot in space. To fulfill this task sensor fusion algorithms are often used, in which data from one or several exteroceptive sensors like, for example, LiDAR, camera, laser ranging sensor or GNSS are fused together with the Inertial Measurement Unit (IMU) measurements to obtain an estimate of the navigation states of the robot. Nonetheless, owing to their particular sensing principles, some exteroceptive sensors are often incapacitated in extreme environmental conditions, like extreme illumination or presence of fine particles in the environment like smoke or fog. Radars are largely immune to aforementioned factors thanks to the characteristics of electromagnetic waves they use. In this thesis, we present Radar-Inertial Odometry (RIO) algorithms to fuse the information from IMU and radar in order to estimate the navigation states of a (Uncrewed Aerial Vehicle) UAV capable of running on a portable resource-constrained embedded computer in real-time and making use of inexpensive, consumer-grade sensors. We present novel RIO approaches relying on the multi-state tightly-coupled Extended Kalman Filter (EKF) and Factor Graphs (FG) fusing instantaneous velocities of and distances to 3D points delivered by a lightweight, low-cost, off-the-shelf Frequency Modulated Continuous Wave (FMCW) radar with IMU readings. We also show a novel way to exploit advances in deep learning to retrieve 3D point correspondences in sparse and noisy radar point clouds.
関連論文
- 組み込みベアメタル向けレーダー慣性オドメトリレーダー慣性オドメトリ
- VGC-RIO: 空間重み付きドップラー速度と局所幾何拘束RCSヒストグラムを統合したレーダー慣性オドメトリレーダー慣性オドメトリ
- 時間遅延がレーダー慣性オドメトリに与える影響レーダー慣性オドメトリ
- ガウス過程による連続速度積分を用いた地面最適化4Dレーダー慣性オドメトリレーダー慣性オドメトリ
- RINO: 非反復推定による高精度・堅牢なレーダー慣性オドメトリレーダー慣性オドメトリ
- 物理情報を活用したレーダー慣性オドメトリ：少ない情報で高精度を実現レーダー慣性オドメトリ
