GitHub topics: lidar-sensors
Amanuel-1/autonomous-drone
A 3D autonomous drone simulation with AI-powered flight capabilities using deep reinforcement learning. Features realistic physics, LiDAR obstacle detection, and a neural network that learns to navigate complex environments through both reinforcement learning and imitation learning from human demonstrations.
Language: TypeScript - Size: 20.9 MB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 1 - Forks: 0
ChevronOne/pc_alignment_tools
Calibration of LiDAR-Sensors - PointCloud Alignment/Registration tools with PCL & ROS
Language: C++ - Size: 22.1 MB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 21 - Forks: 3
SiddhantNadkarni/Sensor-Fusion-Lidar-Object-Detection
Using Euclidiean Clustering and RANSAC to detect Objects in Lidar captured Point Clouds (PCDs)
Language: C++ - Size: 189 MB - Last synced at: over 1 year ago - Pushed at: over 5 years ago - Stars: 28 - Forks: 12
jackyhuynh/kalman_filter_for_localization_using_python
Kalman filters are really good at taking noisy sensor data and smoothing out the data to make more accurate predictions. For autonomous vehicles, Kalman filters can be used in object tracking. A Kalman filter does this by weighing the uncertainty in your belief about the location versus the uncertainty in the lidar or radar measurement. If your belief is very uncertain, the Kalman filter gives more weight to the sensor. If the sensor measurement has more uncertainty, your belief about the location gets more weight than the sensor measurement.
Language: Jupyter Notebook - Size: 831 KB - Last synced at: over 2 years ago - Pushed at: over 4 years ago - Stars: 6 - Forks: 2
