Closed 19855179496 closed 9 months ago
Thank you for your interest in this work. This is a good question! I verified iG-LIO's accuracy on the Apollo dataset: https://developer.apollo.auto/daoxianglake.html. However, this experiment was removed due to the page limitation of RA-L.
The speed of the dataset was around 10~14 m/s, not 20m/s. The results are listed as follows:
RPE per100 meters and time(ms) | Seq | iG-LIO(RPE / time) | Faster-LIO | FastLIO2 | Dist(km) |
---|---|---|---|---|---|
daoxianglake_20190924124848 | 0.096 / 18.12 | 0.109 / 24.71 | 0.101 / 46.42 | 47.973 | |
daoxianglake_20191014142530 | 0.097 / 18.88 | 0.102 / 25.33 | 0.097 / 47.31 | 27.825 | |
daoxianglake_20191021162130 | 0.092 / 18.45 | 0.096 / 26.24 | 0.087 / 48.00 | 41.013 | |
daoxianglake_20191025104732 | 0.092 / 18.42 | 0.094 / 26.06 | 0.092 / 47.41 | 18.734 | |
daoxianglake_20191225153609 | 0.076 / 18.52 | 0.086 / 28.04 | 0.087 / 47.40 | 21.45 |
I think iG-LIO can work well as long as the following conditions are met:
I hope these experiment results and suggestions will help you :smile:
May I ask how this algorithm performs in high-speed motion (20m/s), what is its pose error, and is its accuracy sufficient...