- Видео 53
- Просмотров 49 197
Kenji Koide
Добавлен 28 окт 2019
Видео
GLIM on Jetson Orin Nano
Просмотров 64128 дней назад
Jetson Orin Nano (15W) Configuration : OdometryEstimationGPU SubMapping (GPU) GlobalMapping (GPU) github.com/koide3/glim
![Bundle Adjustment Factor [gtsam_points]](http://i.ytimg.com/vi/tuDV0GCOZXg/mqdefault.jpg)
Bundle Adjustment Factor [gtsam_points]
Просмотров 425Месяц назад
(Coming soon) github.com/koide3/gtsam_points k_koide3
![Continuous Time ICP Factor [gtsam_points]](http://i.ytimg.com/vi/Xv2-qDlzQYM/mqdefault.jpg)
Continuous Time ICP Factor [gtsam_points]
Просмотров 292Месяц назад
(Coming soon) github.com/koide3/gtsam_points k_koide3
![SE3 BSpline Interpolation [gtsam_points]](http://i.ytimg.com/vi/etAI8go3b8U/mqdefault.jpg)
SE3 BSpline Interpolation [gtsam_points]
Просмотров 155Месяц назад
(Coming soon) github.com/koide3/gtsam_points k_koide3
![Incremental VoxelMap Update and Normal Estimation [gtsam_points]](http://i.ytimg.com/vi/gDiKqQDc7yo/mqdefault.jpg)
Incremental VoxelMap Update and Normal Estimation [gtsam_points]
Просмотров 406Месяц назад
(Coming soon) github.com/koide3/gtsam_points k_koide3
![Colored ICP Factor [gtsam_points]](http://i.ytimg.com/vi/xEQmiFV79LU/mqdefault.jpg)
Colored ICP Factor [gtsam_points]
Просмотров 188Месяц назад
(Coming soon) github.com/koide3/gtsam_points k_koide3
![[GLIM] Visual-LiDAR-IMU SLAM on a Drone (NTU-VIRAL)](/img/1.gif)
[GLIM] Visual-LiDAR-IMU SLAM on a Drone (NTU-VIRAL)
Просмотров 1,3 тыс.Месяц назад
(Coming soon) github.com/koide3/glim k_koide3
[GLIM] Flatwall experiment with Livox Avia (Complete point cloud degeneration)
Просмотров 392Месяц назад
(Coming soon) github.com/koide3/glim k_koide3
[GLIM] Mapping with Azure Kinect
Просмотров 739Месяц назад
(Coming soon) github.com/koide3/glim k_koide3
[GLIM] Mapping with various range sensors (Same parameter setting for all)
Просмотров 1,4 тыс.Месяц назад
The same parameters are used for all the sensors. (Coming soon) github.com/koide3/glim k_koide3
[GLIM] Outdoor driving test with Livox MID360 (Processing speed: x14 of real-time)
Просмотров 954Месяц назад
[GLIM] Outdoor driving test with Livox MID360 (Processing speed: x14 of real-time)
[GLIM] Map correction with offline_viewer
Просмотров 531Месяц назад
[GLIM] Map correction with offline_viewer
MegaParticles: 6-DoF Monte Carlo Localization (Closeup View)
Просмотров 6003 месяца назад
MegaParticles: 6-DoF Monte Carlo Localization (Closeup View)
[ICRA2024] MegaParticles : 6-DoF Monte Carlo Localization with One Million Particles
Просмотров 1,5 тыс.3 месяца назад
[ICRA2024] MegaParticles : 6-DoF Monte Carlo Localization with One Million Particles
Scan matching speed comparison (small_gicp vs Open3D)
Просмотров 6034 месяца назад
Scan matching speed comparison (small_gicp vs Open3D)
GLIL robustness test (dynamic objects and motion)
Просмотров 2945 месяцев назад
GLIL robustness test (dynamic objects and motion)
Quadruped robot (MPC-based planning) [Student Project]
Просмотров 2305 месяцев назад
Quadruped robot (MPC-based planning) [Student Project]
Quadruped robot (Gesture-input) [Student Project]
Просмотров 895 месяцев назад
Quadruped robot (Gesture-input) [Student Project]
Quadruped robot (Gesture-input & MPC-based planning) [Student Project]
Просмотров 2915 месяцев назад
Quadruped robot (Gesture-input & MPC-based planning) [Student Project]
MPC-based Path Planning [Student project]
Просмотров 3125 месяцев назад
MPC-based Path Planning [Student project]
Kenji is GOAT!
Nice job!
Yes you feel it.
You can feel it too.
Is GLIM contain location function? if not, is that any shedule for sharing the location function part? Tks in advance.
Can it run on OrangePi5?
I think yes. The configuration shown in the video description uses only ~40% of the CPU resource of Khadas VIM3.
Thank Kenji
where is dataset?
Super impressive. Great work! Thanks for open sourcing it!
Офигенно! Отличная работа!
good job
OMG it's released!
😍
Turning the body, I agree, it’s convenient to control the rotor, but rotate it to go forward is a mockery! It clearly should be in combination with a regular joystick.
I fully agree with you, but it's fun! I decided to create this after playing "Playdate", a handheld game console with a mechanical crank.
❤
❤
Always a big fan of your work. Is this in preparation for a new publication?
Thanks! Yes, it corresponds to our new paper that is going to come out in a few weeks.
Kenji, is this rviz2 on the right side? Your visualization is so cool
Thanks. The left is the usual rviz2 and the right is our original viewer (github.com/koide3/iridescence).
this is sick
Super excited for the release :)
Great work. I'm guessing you are fusing the results of IMU's estimate of position and SFM data from camera. How much drift are you getting as of now?
It fuses all Visual-LiDAR-IMU constraints on a unified factor graph. Because it was an easy setup for the LiDAR, it got almost no drift at all.
@@kenjikoide6076 If LiDAR's also participating in Motion estimation, it sure would be very accurate. I would bet that if you used only the data from LiDAR you would probably get the same estimated motion. I was thinking that LiDAR was used as ground truth.
@@shivavarunadicherla Yes, visual constraints brought only minor accuracy gain in this dataset indeed. This is just a demonstration. What we are truly aiming for is overcoming situations where point clouds become completely degenerate (e.g., tunnels), and we've confirmed that visual constraints greatly improves the reliability in such situations.
There is another one new dataset called MCD. offers higher challenge than this.
Thanks, I'll definitely try MCD. NTU-VIRAL was easy and didn't bring much insight.
@@kenjikoide6076 ntu viral original sequences are easy. The new additional sequence spms one are much tougher. So far only selected few lio can run
Lol i flu this sequences
Trying TRO again?
No, it's accepted to another one :)
GitHub 404 😢
Sorry, we are finalizing the repository, it'll come out in a few weeks.
🥰
Very impressive!
I appreciate your work very much. Can you open source or contact me? I am studying related aspects recently
I appreciate your work very much. Can you open source or contact me? I am studying related aspects recently😍
めっちゃツヨい
Nice :) Which SLAM did you use for that? FAST-LIO or something else?
That was wonderful work. Congarts my master 🙏
Nice Work! Looking forward to your paper being published!
Impressive. Assume this is with a depth camera. Which one?
It's an azure kinect.
and Livox MID360 was used in the outdoor experiment.
Unbelievable!😍
This is soooo cool🎉
I really appreciate your contributions to the open-source community😍😍
Nice! For map based navigation, whenever I tried to match the point cloud to the map, it gave me a wrong pose estimation due to wrong correspondences as there are too many match candidates. So I made the map as pairs of (pose, LiDAR scan) and matched to the LiDAR scan at that pose, which made the "map" file too big. Great work! I should read your paper to re-implement the navigation module.
a $750+ football
amazing work
Wow that was cool!
will code release after publish this paper?
The global localization part is available at: github.com/koide3/hdl_global_localization
@@kenjikoide6076 thanks, it's cool
there are too many monsters................................
awesome!!!! Code release is not considered?
The global localization part is available at: github.com/koide3/hdl_global_localization
Kenji, I guess you fall in fishing these days lol 🎣
C ya in yokohama
Awesome!🥰
これからインタラゲームの新しい時代も感じます。
- -0, why kick. why not use a momentum-controlled ball robot with it to move around? much like GUNDAM hello halo ball
this project is open source? im trying to create a low cost robot with lidar sensor on it, because it will work in underground pipeline. but im no programmer, just a dreammer, and its really hard to start programming