Drivers for receiving LiDAR data and controlling lidar, support Lidar HAP and Mid-360.

You are tasked with the development of an autonomous drone delivery system for light-weight package transportation around the West Cambridge site. By using high-fidelity drone models from the new MathWorks UAV Toolbox, you will develop an autonomous ground control system that coordinates one (or more!) drones around the site. Consider how to incorporate path and task planning, motor and plant control, and sensing & perception for navigation and obstacle avoidance. Evaluation in the realistic UE4 simulation will include simulated sensor data from 3D LIDAR readings and simulated camera input.

IOU Calculation for 2D Quadrilaterals The major functional components of autonomous vehicles are perception, control, planning, system management, and localization. Perception is a process that senses the surrounding environment using various sensors like Radars, LiDARs, Ultrasonic and Cameras sensors. Sensors are designed to extract information from the environment and hence, to perceive the surroundings. • Lidars are used to extract the information on the position and shape of surrounding obstacles within its range and field of view (FOV). • Camera sensor data provides information about the object class. • Radars are used to derive the position and velocity of the obstacles and so on. Multi-sensor fusion integrates the sequence of observations from a number of heterogeneous sensors into a single best estimate of the state of the environment. One of the Sensor Fusion outputs is the IOU (Intersection over Union) or Jaccard index during the object detection. When the object detection is performed through more than one source of sensors (such as Ultrasonic and Camera sensors), the IOU or Jaccard index is calculated to quantify the percent overlap from two different sources of sensors. The basic problem in multi-sensor fusion systems is to integrate a sequence of observations from a number of different sensors into a single best estimate of the state of the environment. In such a case, the IOU helps to identify the overlap area, which is captured from the multi-sensors. For example for the Autonomous Parking Functionality of ADAS (Autonomous Driving Assistance System), the Ultrasonic and Camera sensors are capturing the free space for Ego Vehicle Parking (as shown in the below figure). As per the capability and mounting position of different sensors, the available parking space is captured. The captured area from different sensors may or may not be the same. In that case, the IOU or Jaccard Index helps to quantify the overlap area detected by two different sensors. Figure 1: Practical use case of IOU The IOU or Jaccard Index is calculated as follows: Figure 2: IOU Calculation The IOU (Intersection over Union) value varies between 0 to 1. More the overlap region better the IOU value. Henceforth the confidence in the input data from the sensors increases. Lower the IOU, troubles in deciding the available space for the parking as different sensors are showing different spaces for parking. Figure 3: Confidence decision based on IOU Note: Decision of the High Confidence from calculated IOU value varies from application to application. For example, in some applications, High Confidence can be decided over 0.8 IOU value whereas, in some other applications, High Confidence can be decided over 0.9 IOU value. The IOU calculation can be done over the images or coordinates captured from the different sensors. In addition, the IOU calculation can also be performed considering the captured object as a 2D or 3D object. In this article, I have focused on the IOU calculation based on coordinates received from two different sensors. The captured coordinates would be of 2D Quadrilateral. Refer to the MATLAB Code for the Calculation of IOU using the X and Y coordinates captured from the two different sensors. The point of interest here is in finding the intersection points and identifying the quadrilateral vertices that lie inside another quadrilateral. A glimpse of the MATLAB code results: Figure 4: IOU calculation from MATLAB Code I have considered all the possible conditions for regular/irregular quadrilateral such as complete overlapping, no overlapping, vertices having negative and positive coordinates, and so on. Thank you for reading. I am open to discussion on this topic. Do reach out to me at chetan9chudhari@gmail.com. HAPPY LEARNING!!!

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PIFBot implements SLAM and Navigation with ROS. Hardware base control uses STM32 microcontroller to exchange data with ROS over rosserial protocol. This robot equiped LiDAR and RGB-D Camera that makes it compatible with many SLAM methods.

Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system, which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method.

This project, E2E DriveAI, is a ROS2-based modular framework designed to enable end-to-end autonomous vehicle control using raw sensor data. It integrates exteroceptive sensors, including cameras, LiDAR, and on-board diagnostic (OBD) data, to develop a comprehensive autonomous driving solution.

SDK for LitraTech's next generation device protocol (LDCP or LiDAR Data and Control Protocol, based loosely on JSON-RPC).

基于Qt的Livox激光雷达可视化与控制软件，支持实时点云显示、数据记录和高级可视化功能 | Qt-based visualization and control software for Livox LiDAR devices, supporting real-time point cloud display, data recording, and advanced visualization features.

The goal of the project is to apply Computer Vision and Deep Learning toward Perception problems like lane finding, classifying traffic signs, as well as a full end-to-end algorithm for driving with behavioral cloning. And also to track objects from radar and lidar data with sensor fusion and implement the Localization, Path Planning and Control to navigate and drive the car.

Seperate dual lidar lasers and load the intensity and ring-numbers for better control over lidar data. Using Argoverse dataset.

Python codes for Robot, vpn keyboard control, vpn data exchange, lidar data projection, and much more!

Establish communication with center control system\TCP to get Lidar data\using C#&matlab dll for data process

Developing an autonomous self-driving robot using ROS2 and Python, focusing on odometry, control systems, and sensor fusion. Kalman Filters combine data from LiDAR, IMU, and cameras for enhanced localization. The project improves my skills in robotic control, sensor integration, and autonomous navigation.

Command Line Tools tto Generate a GCP Quality Statistical Report from LIDAR LAS DATA and VERTICAL Control.

A versatile system including software integration for the movement of the rover, receiving data from the 2D lidar, interfacing the 2D lidar data to the master control system, and maneuvering the rover to map different locations of the environment. 3D Lidar data is acquired simultaneously and in real-time.

Remote LiDAR Scanner project for ECE3334 CMPE Project Lab. Built to remotely control and access data from the RPLidar A1M8.

In this project, a model of a differential drive robot (DDR) was designed for use with ROS and Gazebo. The model includes lidar and collision sensors. Additionally, three nodes were coded to obtain laser data and control the movement of the robot.

Project to control and get data from an Ouster OS-1 Lidar

LiDAR Quality Control Suite. A Suite a tools for performing quality control on LiDAR data, developed for the BC provincial government. All tools can be access via a GUI.

Project uses 2D particle filter localization to locate a kidnapped vehicle, given noisy Lidar sensor and control data, and an initial GPS position

Synthetic data generation tool for single-photon LiDAR/ToF imaging of rotating object (drone blades). Features physically-based simulation, parameter control, and 3D visualization.

A robot control system for MTC True Tech Hack 2025 that implements autonomous navigation with obstacle avoidance using LiDAR data, communicates with Webots simulation via UDP, and provides a REST API for robot control and sensor data access.

The subExplore is a submarine tinking to explore areas without human control sanding data to a data center receiver. Datas like: global position, Euler angles, Lidar datas and other informations about his location on the world and the own world.

An autonomous quadcopter system that navigates predefined courses using Lidar, GPS, and an A* path planning algorithm for optimal route completion, with PID control and data analysis features, developed in a simulation environment.

E2E DriveAI, is a ROS2-based modular framework designed to enable end-to-end autonomous vehicle control using raw sensor data. It integrates exteroceptive sensors, including cameras, LiDAR, and on-board diagnostic (OBD) data, to develop a comprehensive autonomous driving solution.

Built and tested an Autonomous Electrified Vehicle (AEV) platform integrating VESC motor control, ROS-based navigation, LiDAR perception, and autonomous driving algorithms. Responsible for motor tuning, data logging, ROS driver modifications, and implementing collision-avoidance and navigation logic.

Programmed Arduino micro-controllers using Embedded C language to control sensors like- Lidar, Thermal sensor, Camera module, Servo motor. Established real-time wireless communication between the robot and the user’s control unit via ESP Wi-Fi module. Plotted the data in 2D map created with Python to map the environment in Real time.

The major task the young engineers is to make a vehicle smarter and to achieve this we need the smartest technique. One such sensor which is can be used for detecting path and obstacles is the LIDAR which can be used instead of normal camera. The steps for achieving this are as follows: The LIDAR will be mounted either over the bonnet or the roof of the vehicle. Lidar takes numerous point from the path it travels and we have to preprocess the data which we obtain from the LIDAR sensor. All the data that the LIDAR senses is send to the micro-processor raspberri pi and is then coupled to a ML model to eliminate the unwanted data. Using various ML (machine learning) approaches such as Faster RNN , Mask RNN and SSD, we will be able sort out the required data with high accuracy. Finally this data is send to a cloud which can further be obtained from the LIDAR 360 software. Using this software we will be able to simulate the specified model types to control all traffic system , lane path identification and smart vehicle system. and thus a magnificent solution can be developed.

Adaptive control of a mecanum robot using a Mixture of Experts with three RL models. A gating network selects the best expert based on RGB camera and 2D LiDAR data. The system runs in Gazebo and ROS2, enabling robust, context-aware navigation and obstacle avoidance.