Tightly Coupled LiDAR-IMU-Odometry Fusion with Continuous-Time Trajectory Representation for Robust Ego-Vehicle Localization
連続時間軌道表現に基づくLiDAR・IMU・オドメトリ密結合融合による頑健な自己位置推定
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20266233
- Paper/Info type
- Proceedings (Autumn)
No.140-26
- Pages
- 1-2(Total 2 p)
- Date of publication
- Oct 2026
- Publisher
- JSAE
- Language
- English
- Event
- 2026 JSAE Annual Congress (Autumn)
Detailed Information
| Author(J) | 1) Naoki Akai, 2) Yasuhiro Akagi, 3) Takayuki Morikawa |
|---|---|
| Author(E) | 1) Naoki Akai, 2) Yasuhiro Akagi, 3) Takayuki Morikawa |
| Affiliation(J) | 1) Nagoya University, 2) Nagoya University, 3) Nagoya University |
| Affiliation(E) | 1) Nagoya University, 2) Nagoya University, 3) Nagoya University |
| Abstract(J) | LIOはLiDARとIMUのみで利用できる一方，車両では運動特性を表すオドメトリの活用が頑健性向上に有効である．本講演では，従来的な離散時間の状態推定システムを連続時間状態推定システムに拡張し，LiDAR・IMU・オドメトリを各特性に応じてシームレスに融合する車両位置推定技術について述べる． Translation |
| Abstract(E) | LIO can be used with only a LiDAR and an IMU, while odometry, which represents vehicle motion characteristics, is effective for improving robustness in vehicle applications. This presentation describes a robust vehicle localization technique that extends conventional discrete-time state estimation systems to continuous-time state estimation systems, enabling seamless fusion of LiDAR, IMU, and odometry according to their respective characteristics. |
