• Stars
    star
    607
  • Rank 73,845 (Top 2 %)
  • Language
    C++
  • License
    GNU General Publi...
  • Created over 3 years ago
  • Updated almost 2 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

This repository provides implementation of an incremental k-d tree for robotic applications.

ikd-Tree

ikd-Tree is an incremental k-d tree designed for robotic applications. The ikd-Tree incrementally updates a k-d tree with new coming points only, leading to much lower computation time than existing static k-d trees. Besides point-wise operations, the ikd-Tree supports several features such as box-wise operations and down-sampling that are practically useful in robotic applications.

What does ikd-Tree support?

  • Build a balanced k-d tree - Build()

  • Dynamically insert points to or delete points from the k-d tree - Add_Points() / Delete_Points()

  • Delete points inside given axis-aligned bounding boxes - Delete_Point_Boxes()

  • K Nearest Neighbor Search with range limitation - Nearest_Search()

  • Acquire points inside a given axis-aligned bounding box on the k-d tree - Box_Search()

  • Acquire points inside a ball with given radius on the k-d tree - Radius_Search()

User Manual

Developers

Related paper

If you are using any code of this repo in your research, please cite at least one of the articles as following:

  • ikd-Tree
@article{cai2021ikd,
  title={ikd-Tree: An Incremental KD Tree for Robotic Applications},
  author={Cai, Yixi and Xu, Wei and Zhang, Fu},
  journal={arXiv preprint arXiv:2102.10808},
  year={2021}
}
  • FAST-LIO2
@article{xu2022fast,
  title={Fast-lio2: Fast direct lidar-inertial odometry},
  author={Xu, Wei and Cai, Yixi and He, Dongjiao and Lin, Jiarong and Zhang, Fu},
  journal={IEEE Transactions on Robotics},
  year={2022},
  publisher={IEEE}
}

Build & Run demo

1. How to build this project

cd ~/catkin_ws/src
git clone [email protected]:hku-mars/ikd-Tree.git
cd ikd-Tree/build
cmake ..
make -j 9

2. Run our examples

Note: To run Example 2 & 3, please download the PCD file (HKU_demo_pointcloud) into${Your own directory}/ikd-Tree/materials

cd ${Your own directory}/ikd-Tree/build
# Example 1. Check the speed of ikd-Tree
./ikd_tree_demo
# Example 2. Searching-points-by-box examples
./ikd_Tree_Search_demo
# Example 3. An aysnc. exmaple for readers' better understanding of the principle of ikd-Tree
./ikd_tree_async_demo

Example 2: ikd_tree_Search_demo

Box Search Result Radius Search Result

Points returned from the two search methods are shown in red.

Example 3: ikd_tree_Async_demo

Original Map:

Box Delete Results:

Points removed from ikd-Tree(red) Map after box delete

This example is to demonstrate the asynchronous phenomenon in ikd-Tree. The points are deleted by attaching 'deleted' on the tree nodes (map shown in the ) instead of being removed from the ikd-Tree immediately. They are removed from the tree when rebuilding process is performed. Please refer to our paper for more details about delete and rebuilding.

Acknowledgments

  • Thanks Marcus Davi for helps in templating the ikd-Tree for more general applications.

  • Thanks Hyungtae Lim 임형태 for providing application examples on point clouds.

License

The source code of ikd-Tree is released under GPLv2 license. For commercial use, please contact Mr. Yixi CAI ([email protected]) or Dr. Fu ZHANG ([email protected]).

More Repositories

1

FAST_LIO

A computationally efficient and robust LiDAR-inertial odometry (LIO) package
C++
2,549
star
2

r3live

A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package
C++
1,958
star
3

loam_livox

A robust LiDAR Odometry and Mapping (LOAM) package for Livox-LiDAR
C++
1,435
star
4

FAST-LIVO

A Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry (LIVO).
C++
1,086
star
5

livox_camera_calib

This repository is used for automatic calibration between high resolution LiDAR and camera in targetless scenes.
C++
863
star
6

LiDAR_IMU_Init

[IROS2022] Robust Real-time LiDAR-inertial Initialization Method.
C++
834
star
7

Point-LIO

C++
745
star
8

r2live

R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping package
C++
721
star
9

BALM

An efficient and consistent bundle adjustment for lidar mapping
C++
700
star
10

ImMesh

ImMesh: An Immediate LiDAR Localization and Meshing Framework
C++
590
star
11

STD

A 3D point cloud descriptor for place recognition
C++
548
star
12

VoxelMap

[RA-L 2022] An efficient and probabilistic adaptive voxel mapping method for LiDAR odometry
C++
479
star
13

mlcc

Fast and Accurate Extrinsic Calibration for Multiple LiDARs and Cameras
C++
479
star
14

FAST-LIVO2

FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry
471
star
15

HBA

[RAL 2023] A globally consistent LiDAR map optimization module
C++
437
star
16

IKFoM

A computationally efficient and convenient toolkit of iterated Kalman filter.
C++
420
star
17

M-detector

C++
362
star
18

LTAOM

C++
325
star
19

ROG-Map

C++
294
star
20

MARSIM

MARSIM: A light-weight point-realistic simulator for LiDAR-based UAVs
C++
283
star
21

D-Map

D-Map provides an efficient occupancy mapping approach for high-resolution LiDAR sensors.
C++
280
star
22

decentralized_loam

207
star
23

joint-lidar-camera-calib

Joint intrinsic and extrinsic LiDAR-camera calibration.
C++
194
star
24

SLAM-HKU-MaRS-LAB

In this repository, we present our research works of HKU-MaRS lab that related to SLAM
191
star
25

Voxel-SLAM

C++
185
star
26

Swarm-LIO2

Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarms
158
star
27

dyn_small_obs_avoidance

C++
154
star
28

IPC

Integrated Planning and Control for Quadrotor Navigation in Presence of Sudden Crossing Objects and Disturbances
C++
147
star
29

btc_descriptor

137
star
30

PULSAR

C++
102
star
31

lidar_car_platfrom

48
star
32

iBTC

39
star
33

crossgap_il_rl

Python
38
star
34

multi_lidar_calib

28
star
35

Livox_handheld

25
star
36

mapping_eval

2
star