ojindal

Pinned Repositories

Particle_filter_SLAM
Implemented simultaneous localization and mapping (SLAM) using odometry, 2-D LiDAR scans, and stereo camera measurements from an autonomous car to localize the robot and build a 2-D occupancy grid map of the environment.
Language:Python10
Motion-Planning-Moving-target
Motion Planning to intercept a moving target (Search based planning)
Language:Python00
Planning_and_Control-Mbot_Mega_with_RB5
This project focuses on path planning and implementing PID control on the mBot mega powered by Qualcomm RB5 to address two specific requirements about the nature of the path traced i.e., Distance optimality and maximum safety.
Language:C++00
Traffic_Sign_Recognition-CNN
Language:Jupyter Notebook01
Roomba_Motion
The aim is to design a Roomba-like system capable of mapping its environment and providing a certain level of area coverage for the supposed cleaning action, similar to a Roomba robot
Language:C++00
Motion_planning-Door-Key
Autonomous navigation in a ‘Door & Key’ environment
Language:Python00
Visual-Inertial-SLAM
Implemented visual-inertial simultaneous localization and mapping (SLAM) using an extended Kalman filter (EKF) in Python. Provided data: Synchronized measurements from an inertial measurement unit (IMU) and a stereo camera and the intrinsic camera calibration and the extrinsic calibration between the two sensors, specifying the transformation from the IMU to the left camera frame.
Language:Python20
Bin_detection
Deployed a classification model based on Gaussian Discriminant Analysis to recognize recycling-bin in real-world surroundings.
Language:Python00
ojindal
Orish Jindal portfolio
00
Omron_AMR_ROS2
Omron Ros2
Language:Python00

ojindal's Repositories

ojindal/xarm_docker_extra
Language:Python
ojindal/xarm_ros2
ROS2 developer packages for robotic products from UFACTORY
ojindal/Omron_AMR_ROS2
Omron Ros2
Language:Python
ojindal/omron_docker
Omron docker wrapping ros2 foxy
ojindal/params.yml
parameters file
ojindal/ojindal
Orish Jindal portfolio
ojindal/Particle_filter_SLAM
Implemented simultaneous localization and mapping (SLAM) using odometry, 2-D LiDAR scans, and stereo camera measurements from an autonomous car to localize the robot and build a 2-D occupancy grid map of the environment.
Language:Python1
ojindal/Traffic_Sign_Recognition-CNN
Language:Jupyter Notebook1
ojindal/Roomba_Motion
The aim is to design a Roomba-like system capable of mapping its environment and providing a certain level of area coverage for the supposed cleaning action, similar to a Roomba robot
Language:C++
ojindal/SLAM-Mbot_Mega_with_RB5
Implementation of Kalman Filter, a version of the Simultaneous Localization and Mapping (SLAM) technique and evaluation of its performance over variations in robot trajectory.
Language:C++
ojindal/Motion_planning-Door-Key
Autonomous navigation in a ‘Door & Key’ environment
Language:Python
ojindal/Motion-Planning-Moving-target
Motion Planning to intercept a moving target (Search based planning)
Language:Python
ojindal/Planning_and_Control-Mbot_Mega_with_RB5
This project focuses on path planning and implementing PID control on the mBot mega powered by Qualcomm RB5 to address two specific requirements about the nature of the path traced i.e., Distance optimality and maximum safety.
Language:C++
ojindal/rb5_ros
ojindal/Bin_detection
Deployed a classification model based on Gaussian Discriminant Analysis to recognize recycling-bin in real-world surroundings.
Language:Python
ojindal/Visual-Inertial-SLAM
Implemented visual-inertial simultaneous localization and mapping (SLAM) using an extended Kalman filter (EKF) in Python. Provided data: Synchronized measurements from an inertial measurement unit (IMU) and a stereo camera and the intrinsic camera calibration and the extrinsic calibration between the two sensors, specifying the transformation from the IMU to the left camera frame.
Language:Python2
ojindal/SinglyLinkedList
Singly linked list