Implementing Extended Kalman Filter in C++
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Updated
Apr 17, 2017 - C++
Implementing Extended Kalman Filter in C++
A LiDAR 3D Mapping system which is very low cost at 5000₹(~80$) made using arduino.
Utilize an Extended Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements
Utilize an Unscented Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements
Extended Kalman Filter on LiDAR and Radar sensor feed
Implemented sensor fusion algorithm using an extended Kalman filter that tracks nearby moving objects using RADAR and LIDAR measurements.
Implementation using only LASER (LIDAR) measurements to predict a pedestrian
Simulation of a LiDAR (Light Detection and Ranging) sensor
Assignments and Projects for the UNSW Advanced Autonomous Systems Term 1 2019
Lidar Obstacle Detection
Code and documents to support the Thesis: Progress Towards LiDAR Based Bicycle Detection in Urban Environments Edit Add topics
Containerized ROS node that communicates with x4 lidar via USB pass though from the docker host
.net wrapper of YDLidar sdk
The Light Imaging Detection and Ranging (LIDAR) is a method for measuring distances (ranging) by illuminating the target with laser light and measuring the reflection with a sensor. The LIDAR Sensor escalates the entire mechanism with great efficiency which is notified with process and main activation codes.
An Extended Kalman Filter Project
Implementation of the Extended Kalman Filter in C++
High-performance localization software for autonomous vehicles. A particle filter is combined with a map to localize a vehicle.
Utilized an Extended Kalman Filter and Sensor Fusion to estimate the state of a moving object of interest with noisy lidar and radar measurements. The project involved utilzing lidar data (Point Cloud) for position and radar data (Doppler) for radial velocity.
Explore and analyze LiDAR data from Global Ecosystem Dynamics Investigation (GEDI) of NASA
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