Autonomous Driving Platform - LSR200Auto

07 Outdoor Navigation & LiDAR Perception 

 

     Multi-Line LiDAR Integration: Designed for low-speed autonomous vehicles, our 16-line mechanical LiDAR utilizes TOF (Time-of-Flight) ranging.

 

 

08 Autonomous Power Management (Optional)

 

09 3D Ultrasonic Blind-Spot Detection System(Optional)

     Equipped with 4 front-facing and 2 rear-facing ultrasonic sensors integrated into the chassis, the robot effectively fills in the perceptual blind spots that fall outside the LiDAR’s scanning plane, completely eliminating collision risks in tight spaces.

     Note: The ultrasonic sensor array comes standard only on the Flagship Independent Suspension model.

 

 

Key Feature Introduction (Fully Open Source)

1.RTAB-Map 3D VSLAM & LiDAR Integration

    Supports RTAB-Map pure vision mapping and LiDAR-vision fusion mapping. Fully compatible with ROS 1 and ROS 2 for versatile 3D environment reconstruction.

 

 

 

 

2.Classic 2D LiDAR Mapping & Navigation

    ROS 2: Supports Gmapping, Cartographer, and slam_toolbox.

    ROS 1: Supports Gmapping, Hector, Karto, and Cartographer.

    Navigation: Features autonomous point-to-point navigation, multi-point waypoints, and dynamic obstacle avoidance.

 

 

 

3.ORB-SLAM2 Visual Mapping

    Features the open-source ORB-SLAM2 framework for real-time camera pose

    estimation and sparse 3D reconstruction. Provides real-scale metric information when used in RGB-D mode.

 

 

 

4.ROS QT Graphical User Interface(GUI)

    Deployed with a dedicated QT-based GUI for "one-click" ROS activation. Provides

    intuitive real-time feedback on robot velocity, battery status, and system health.

 

 

 

 

5.YOLO Object Detection

    ROS 1: Powered by YOLO.v3 for general object, traffic sign, and gesture recognition.

    ROS 2: Powered by YOLO.v8 and YOLOv11 for state-of-the-art object detection and custom model training support.

 

 

 

6.LLM Deployment

    Supports both offline local deployment and online cloud-based integration of Large

    Language Models for advanced reasoning and interaction.

 

 

 

7.Depth-Based Visual Following

    Utilizes depth cameras to calculate target distance and bearing for smooth, real-time robot following.

 

 

 

8.KCF Target Tracking

    Employs Kernelized Correlation Filters (KCF) via the depth camera to identify and track objects with fixed visual features.

 

 

9.AR Tag Recognition & Following

    Detects and tracks the 6-DOF pose of AR Tags, allowing for tag-following behaviors and expanded tag-based localization.

 

 

 

10.RRT Autonomous Exploration

     Enables fully autonomous mapping using the RRT algorithm. The robot explores, maps, saves the data, and returns to the starting point without human intervention.

 

 

 

11.Web-Based Camera Monitoring

     Remotely view the robot's live camera feed through any PC browser for quick deployment of remote surveillance tasks.

 

 

 

12.RGB Camera Line Following

     Enables the robot to follow ground lines via RGB vision. Integrated with LiDAR to ensure automatic obstacle avoidance during line-following missions.

 

 

 

13.LiDAR-Based Following

     Scans the environment for nearby obstacles and intelligently selects the nearest target for the robot to follow.

 

 

 

14.LiDAR Angle Masking

     Optimized via SDK to allow custom angle shielding/masking for all supported LiDAR models.

 

 

15.TTS (Text-to-Speech) Interaction

     Enables full human-machine interaction through TTS technology, with support for expanded iFLYTEK online voice dialogue features.

 

 

 

16.TEB & DWA Path Planning

     Includes detailed video tutorials and Python-based "mini-games" to help users learn navigation path planning from the ground up.

 

 

 

17.DWB / MPPI / RPP Controllers

     Provides three ready-to-use controller plugins (DWB, MPPI, and RPP) tailored for different scenarios and various robot footprints.

 

 

 

18.Full Coverage Path Planning

     Automatically generates a path that covers an entire user-specified area, ideal for cleaning or inspection robots.

 

 

 

19.Chassis Kinematics Analysis

     Provides comprehensive kinematics analysis for various chassis types: Ackermann, Differential, Tracked, Mecanum, Omnidirectional, and 4WD.

 

 

 

20.Path Recording & Playback

     Record manual trajectories and reproduce them autonomously using the Nav2 navigation framework.

 

 

 

21.Comprehensive URDF Models

     Includes high-fidelity URDF models that accurately match the physical robot's dimensions and properties.

 

 

 

22.ROS Mobile APP for Navigation

     A dedicated app for controlling the ROS environment, supporting motion control, mapping, and navigation tasks.

 

 

 

23.Hardware Tuning & Parameter APP

     Supports Android and iOS. Features real-time parameter tuning, gravity-sensing control, and waveform visualization.

 

 

 

24.Wireless Code Flashing (Bluetooth)

     Standard Bluetooth module allows for remote debugging and "second-level" code flashing via the dedicated host computer, simplifying secondary development.

 

 

25.3D Reconstruction for Real-World Autonomous Driving

     Upgrade with an optional multi-line LiDAR to unlock outdoor 3D mapping and environment reconstruction, bringing your development platform incredibly close to full-scale autonomous vehicles.

 

 

26.Autoware.universe Multi-Point Navigation Routines

     Features pre-configured Autoware.universe multi-point navigation examples. By utilizing Python scripts to dispatch sequential waypoint commands, the system achieves smooth, continuous multi-point autonomous navigation across complex routes.

 

 

27.Using Latitude and Longitude Data for Initial Positioning in ndt_matching

      GNSS data can be used to set the initial positioning information of the vehicle, or to reset the location information when there is a large deviation between lidar positioning and map matching.