RitikaAvadhanula's repositories
LIDAR-data-processing-p1-udacity-sensorfusion
The project is created to demonstrate the OBJECT DETECTION PIPELINE used in autonomous mobile robots using LIDAR. The project has successfully implemented the three steps used in point cloud data processing namely: FILTERATION, SEGMENTATION, and CLUSTERING. And at last, rendered the processed data using Point Cloud Library
apriltag_ros
A ROS wrapper of the AprilTag 3 visual fiducial detector
CameraLidarFusion-udacity-sensorfusion
Comparative study of various 2D keypoint feature detection. Additionally a comparative study was done to calculate time to collide for Lidar data and keypoints detected by various methods.
CubeSat_Rendezvous
This repo contains code of a controller design for two satellite approach. It is assumed they have electromagnetic dipoles to achieve the docking. I have designed a LQR and a SMC controller for the system, whose parameters are trimmed using GA in MATLAB. Additionally, It contains code to simulate the trajectory of a sat around earth, the sun's trajectory around the earth.
HomeService-Udacity-Robot
This project simulates a pick-up and drop-off on the robot that was created in my previous project as part of my degree. It uses actionlib, Rviz, srv and movebase of ROS.
hw2-MAS
Q-learning on a gridworl
mobile_robotics_hw
turtlebot3 class assignments - planning, contrl and perception
RADAR-TargetGenerationAndDetection-MATLAB-UdacitySensorFusion
A beat signal is created by mixing the transmitting and the recieving FMCW signals. Range and Doppler map was generated using MATLAB's 2D FFT function. Later I tried 2D CFAR algo to remove false detections
ritikaavadhanula
Personal portfolio website for Ritika Avadhanula generated with the help of a template built on JavaScript and Sass by RaynFitzgerald
ROSbasics-udacity-RoboticsSoftware
Built a ROS world along with robot in the URDF format. Later used ROS amcl package to localize the robot in a known map.
Udacity_Sensor_Fusion_Nanodegree
My work from the Udacity Sensor Fusion Nanodegree Program
UKF-LIDARandRADARfusion-VehicleDetection-UdacitySensorFusion
an Unscented Kalman Filter to estimate the state of multiple cars on a highway using noisy lidar and radar measurements with RMSE values that are lower that the tolerance