HCTO is designed for prior map constraction for robot "last-mile" delivery using wearable device

@article{li2024hcto,
  title={HCTO: Optimality-aware LiDAR inertial odometry with hybrid continuous time optimization for compact wearable mapping system},
  author={Li, Jianping and Yuan, Shenghai and Cao, Muqing and Nguyen, Thien-Minh and Cao, Kun and Xie, Lihua},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={211},
  pages={228--243},
  year={2024},
  publisher={Elsevier}
}

Experimental resuls of HCTO-LIO (Click the gif for a non-speeding-up video)

1. Evaluation in Vicon Room

1.1 Seq 1 (Walking 🚶)

More experiments are listed on Youtube.

2. Evaluation in Indoor Environment

2.1 Seq 1 (Running 🏃)

2.2 Seq 2 (Running 🏃)

2.3 Seq 3 (Running 🏃)

3. Evaluation in NTU Campus

3.1 Site1 - Seq 1 (Walking 🚶)

3.2 Site1 - Seq 2 (Running 🏃)

3.3 Site1 - Seq 3 (Walking 🚶 & Running 🏃)

3.4 Site2 (Multi-level Auditorim) (Walking 🚶 & Running 🏃)

4. Evaluation in WHU-Helmet dataset (https://github.com/kafeiyin00/WHU-HelmetDataset)

4.1 Subway station (Walking 🚶)

4.2 Carpark (Walking 🚶)

5. Degenerated scenes in apartment

5.1 Multi-level apartment (Running 🏃)

5.2 Long corridor inside apartment (Walking 🚶)