| JunshengFu/tracking-with-Extended-Kalman-Filter |
451 |
|
0 |
0 |
over 4 years ago |
0 |
|
5 |
mit |
C++ |
| Object (e.g Pedestrian, vehicles) tracking by Extended Kalman Filter (EKF), with fused data from both lidar and radar sensors. |
| kostaskonkk/datmo |
225 |
|
0 |
0 |
over 2 years ago |
0 |
|
3 |
bsd-2-clause |
C++ |
| Detection and Tracking of Moving Objects (DATMO) using sensor_msgs/Lidar. |
| JunshengFu/tracking-with-Unscented-Kalman-Filter |
100 |
|
0 |
0 |
over 4 years ago |
0 |
|
2 |
|
C++ |
| Object (e.g Pedestrian, biker, vehicles) tracking by Unscented Kalman Filter (UKF), with fused data from both lidar and radar sensors. |
| udacity/SFND_3D_Object_Tracking |
73 |
|
0 |
0 |
about 4 years ago |
0 |
|
0 |
|
C++ |
| YoshuaNava/icpslam |
37 |
|
0 |
0 |
over 6 years ago |
0 |
|
1 |
|
C++ |
| A basic SLAM system that employs 2D and 3D LIDAR measurements |
| TKJElectronics/XV11Lidar_STM32F429 |
32 |
|
0 |
0 |
over 11 years ago |
0 |
|
0 |
gpl-2.0 |
C |
| Neato XV-11 LIDAR connected to an STM32F429IDISCOVERY board for 360-degree range scanning and measurement |
| ser94mor/sensor-fusion |
21 |
|
0 |
0 |
almost 7 years ago |
0 |
|
5 |
gpl-3.0 |
C++ |
| Filters: KF, EKF, UKF || Process Models: CV, CTRV || Measurement Models: Radar, Lidar |
| ChenBohan/Udaicty-CarND-State-Estimation-02-Lidar-and-Radar-Fusion-with-EKF |
19 |
|
0 |
0 |
almost 6 years ago |
0 |
|
0 |
|
C++ |
| Udacity Self-Driving Car Engineer Nanodegree - Term 2 - Lesson 6 - Lidar and Radar Fusion with EKF in C++. |
| OanaGaskey/Extended-Kalman-Filter |
14 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
mit |
C++ |
| Sensor fusion algorithm using LiDAR and RADAR data to track moving object, predicting and updating dynamic state estimation. |
| esrlabs/velodyne |
13 |
|
0 |
0 |
almost 9 years ago |
0 |
|
0 |
mit |
Python |
| Velodyne Lidar Python Library |
