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LSR300 Indoor Differential Service Robot Platform – Mini & Pro
- 100% Open-Source Code
- Professional Technical Support
- Comprehensive Development Resources
- For bulk orders, institutional procurement, or corporate purchasing, please contact us for a formal quotation and procurement support.
The LSR300 Series is a fully open-source differential-drive service robot platform for advanced ROS research, autonomous navigation, 3D perception, embodied AI, service robotics, and industrial mobile-robot development. Available in LSR300 Mini and LSR300 Pro versions, the platform combines independent suspension, dual TOF LiDARs, dual depth cameras, ultrasonic obstacle sensing, NVIDIA Jetson computing, automatic recharging capability, and optional multi-line LiDAR.
- Two chassis sizes: LSR300 Mini and LSR300 Pro
- Differential drive with 2 powered wheels and 4 passive/support wheels
- Independent suspension for indoor and mixed-environment mobility research
- Maximum speed up to 2.0 m/s and approximately 13 hours endurance
- Payload: 20 kg for Mini and 60 kg for Pro
- Dual TOF LiDARs, dual depth cameras, and multi-point ultrasonic obstacle sensing
- Jetson Orin Nano Super (8 GB) or Orin NX Super (16 GB) controller options
- Optional C16 or C32 multi-line LiDAR for 3D mapping and autonomous-driving research
- ROS 1 / ROS 2 development resources covering SLAM, navigation, perception, tracking, planning, and AI integration
Product Overview
The LSR300 Series is designed for researchers and engineering teams that need a higher-end mobile robot platform with integrated sensing, onboard AI computing, autonomous navigation, and open secondary-development resources. The Mini and Pro versions share the same overall software architecture while offering different chassis sizes and payload capacities.
LSR300 Mini is the more compact platform, measuring 530 × 450 × 425 mm with a maximum payload of 20 kg. LSR300 Pro uses a larger 706 × 576 × 433 mm chassis and increases the maximum payload to 60 kg. Both versions use 24 V 20 Ah LiFePO4 battery systems and provide approximately 13 hours of endurance.
For perception and autonomous operation, both platforms integrate two TOF LiDARs and two depth cameras. The Mini version includes five ultrasonic sensors, while the Pro version includes six. Optional C16 or C32 multi-line LiDAR can be selected for 3D point-cloud perception, outdoor mapping, and autonomous-driving research.
LSR300 Mini vs. LSR300 Pro
| Parameter | LSR300 Mini | LSR300 Pro |
|---|---|---|
| Drive Structure | Differential drive | Differential drive |
| Wheel Layout | 2 drive wheels + 4 support wheels | 2 drive wheels + 4 support wheels |
| Drive Motor | 5-inch hub motor | 8-inch hub motor |
| Maximum Speed | 2.0 m/s | 2.0 m/s |
| Maximum Payload | 20 kg | 60 kg |
| Dimensions | 530 × 450 × 425 mm | 706 × 576 × 433 mm |
| Weight | 51 kg | 56 kg |
| Endurance | Approx. 13 hours | Approx. 13 hours |
| TOF LiDAR | 2 | 2 |
| Depth Cameras | 2 | 2 |
| Ultrasonic Sensors | 5 | 6 |
| Battery | 24 V 20 Ah LiFePO4 | 24 V 20 Ah LiFePO4 |
Onboard Compute Options
| Controller | Orin Nano Super (8 GB) | Orin NX Super (16 GB) |
|---|---|---|
| CPU | 6-core Arm Cortex-A78AE v8.2 | 8-core Arm Cortex-A78AE v8.2 |
| GPU | NVIDIA Ampere, 1024 CUDA cores, 32 Tensor Cores | NVIDIA Ampere, 1024 CUDA cores, 32 Tensor Cores |
| AI Performance | 67 TOPS | 157 TOPS |
| Memory | 8 GB LPDDR5 | 16 GB LPDDR5 |
| Storage | 256 GB SSD | 256 GB SSD |
| Network | GigE, M.2 PCIe | GigE, M.2 PCIe |
Multi-Line LiDAR Options
| Parameter | C16 | C32 |
|---|---|---|
| Channels | 16 | 32 |
| Measurement Range | Selectable 70–150 m | 150 m |
| Ranging Principle | Pulsed TOF | Pulsed TOF |
| Horizontal FOV | 360° | 360° |
| Vertical FOV | 30° | 30° |
| Frame Rate | 5 / 10 / 20 Hz | 5 / 10 / 20 Hz |
| Protection Rating | IP67 | IP67 |
ROS, SLAM & AI Development
The LSR300 development resources cover RTAB-Map 3D VSLAM, 2D LiDAR mapping and navigation, ORB-SLAM2, Nav2, YOLO object detection, depth-based following, KCF tracking, AR-tag following, RRT autonomous exploration, path recording and playback, coverage planning, multiple navigation-controller examples, URDF models, mobile control tools, and optional 4G remote operation.
The platform can also be used for local or cloud-connected large-language-model integration and higher-level embodied-AI research. Practical AI model size, latency, and real-time performance depend on the selected onboard computer and software configuration.
Which Model Should I Choose?
LSR300 Mini
Choose the Mini version when installation space is limited and your payload requirement is within 20 kg. It retains the core LSR300 perception and development architecture while using a more compact chassis.
LSR300 Pro
Choose the Pro version when a larger chassis and higher payload capacity are required. The Pro platform is rated for up to 60 kg payload and includes an additional ultrasonic sensing point compared with the Mini.
C16 vs. C32 Multi-Line LiDAR
Select no multi-line LiDAR for primarily indoor 2D navigation and RGB-D development. Choose C16 or C32 when your research requires 3D point clouds, wider outdoor perception, environment reconstruction, or autonomous-driving experiments.
Applications
Service Robots & Indoor AMRs
Use the LSR300 as a research base for service robots, indoor delivery robots, autonomous mobile robots, laboratory logistics, and human-robot interaction projects.
3D Mapping & Autonomous Driving Research
Optional C16/C32 LiDAR and NVIDIA compute support 3D reconstruction, outdoor mapping, point-cloud processing, and low-speed autonomous-driving research.
Embodied AI & Computer Vision
Dual depth cameras and NVIDIA Orin compute provide a development base for object detection, target tracking, semantic interaction, visual following, multimodal perception, and LLM-enabled robotics experiments.
Need Help Selecting?
For a smaller and lower-cost indoor differential research platform, compare the LSR100 indoor differential service robot platform. For outdoor autonomous-driving research with RTK and vehicle-oriented sensing, compare the LSR200Auto autonomous driving platform. You can also browse the Research Robot Platforms collection.
Key Features & Open-Source Development Resources
The LSR300 Series includes a broad set of ROS, SLAM, navigation, perception and secondary-development resources. Feature availability and performance depend on the selected controller, sensors, LiDAR configuration and optional modules.
1. RTAB-Map 3D VSLAM & LiDAR Integration
Supports RTAB-Map vision-only mapping and LiDAR–vision fusion workflows for 3D environment reconstruction in ROS 1 and ROS 2.
2. Classic 2D LiDAR Mapping & Navigation
Provided ROS resources include Gmapping, Cartographer and slam_toolbox for ROS 2, plus Gmapping, Hector, Karto and Cartographer for ROS 1. Navigation examples include point-to-point navigation, multi-point waypoints and dynamic obstacle avoidance.
3. ORB-SLAM2 Visual Mapping
Includes ORB-SLAM2 resources for real-time camera pose estimation and sparse 3D reconstruction. RGB-D mode provides metric-scale depth information for visual mapping experiments.
4. ROS Qt Graphical User Interface
A Qt-based ROS interface provides convenient launch controls and real-time status feedback such as robot velocity, battery status and system information.
5. YOLO Object Detection
Perception resources include YOLOv3 workflows for ROS 1 and YOLOv8 / YOLO11 workflows for ROS 2, supporting object detection and custom-model experiments.
6. LLM Deployment
Supports development workflows for local offline or cloud-connected large-language-model integration. Available performance depends on the selected onboard computing platform and model.
7. Depth-Based Visual Following
Uses RGB-D depth information to estimate target distance and bearing for real-time visual-following experiments.
8. KCF Target Tracking
Includes Kernelized Correlation Filter (KCF) tracking resources for identifying and tracking targets with stable visual features.
9. AR Tag Recognition & Following
Supports 6-DOF AR-tag pose detection for tag-following behaviors and tag-based localization experiments.
10. RRT Autonomous Exploration
Includes an RRT-based autonomous exploration workflow for map exploration, map saving and return-to-start experiments.
11. Web-Based Camera Monitoring
Provides browser-based access to the robot camera stream for remote monitoring and development tasks.
12. RGB Camera Line Following
Supports RGB-vision line following with LiDAR-assisted obstacle avoidance for line-following research and demonstrations.
13. LiDAR-Based Following
Uses LiDAR scans for nearby-target selection and following experiments.
14. LiDAR Angle Masking
SDK-based LiDAR angle masking allows selected scan sectors to be filtered according to the sensor and application configuration.
15. TEB & DWA Path Planning
Includes learning resources and examples for TEB and DWA local path-planning methods, together with Python-based exercises and video tutorials.
16. DWB / MPPI / RPP Controllers
Provides examples using DWB, MPPI and Regulated Pure Pursuit (RPP) controller plugins for different navigation scenarios and robot footprints.
17. Full Coverage Path Planning
Supports generation of coverage paths for user-defined areas, useful for research on cleaning, inspection and systematic-area traversal.
18. Chassis Kinematics Analysis
Development resources include kinematic analysis examples for differential, Ackermann, tracked, Mecanum, omnidirectional and 4WD chassis types.
19. Path Recording & Playback
Manual trajectories can be recorded and replayed autonomously using Nav2-based navigation workflows.
20. Comprehensive URDF Models
Includes URDF models for simulation, visualization and robot-software development based on the physical platform geometry.
21. ROS Mobile App for Navigation
A mobile application is provided for supported ROS motion-control, mapping and navigation workflows.
22. Hardware Tuning & Parameter App
Android and iOS tools support real-time parameter tuning, gravity-sensing control and waveform visualization for supported hardware functions.
23. Optional 4G Remote Module
An optional 4G communication module can be used for long-range video transmission and remote-control development over cellular networks.
24. 3D Reconstruction for Autonomous-Driving Research
An optional multi-line LiDAR extends the platform to outdoor 3D mapping, point-cloud perception and environment-reconstruction research.
Frequently Asked Questions
What is the difference between LSR300 Mini and LSR300 Pro?
LSR300 Mini is the more compact version with a 530 × 450 × 425 mm chassis and a maximum payload of 20 kg. LSR300 Pro measures 706 × 576 × 433 mm and increases the maximum payload to 60 kg. Both share the same general open-source development architecture.
What is the maximum speed and battery endurance?
Both Mini and Pro have a maximum speed of 2.0 m/s and approximately 13 hours of endurance under the stated operating conditions.
What battery does the LSR300 use?
a 24 V 20000 mAh LiFePO4 battery for both Mini and Pro.
Which onboard computers are available?
Controller options are Jetson Orin Nano Super (8 GB) and Jetson Orin NX Super (16 GB), both with 256 GB SSD storage.
What sensors are included on the base platform?
Both versions include two TOF LiDARs and two depth cameras. The Mini uses five ultrasonic sensors, while the Pro uses six.
What multi-line LiDAR options are available?
You can select no multi-line LiDAR, C16, or C32. C16 is a 16-channel unit and C32 is a 32-channel unit. Both support 360° horizontal scanning and 5 / 10 / 20 Hz frame rates.
Does the LSR300 support ROS 1 and ROS 2?
Yes. Development resources include ROS 1 and ROS 2 workflows for SLAM, navigation, perception, tracking, robot models, and secondary development.
Does it support 2D and 3D SLAM?
Yes. The development resources include 2D LiDAR SLAM, RTAB-Map 3D mapping, ORB-SLAM2, and LiDAR–vision fusion workflows. The exact workflow depends on the installed sensors and compute configuration.
Can I use the LSR300 for object detection and visual tracking?
Yes. Perception resources include YOLO-based object detection, RGB-D following, KCF tracking, AR-tag recognition, and other visual-perception examples.
Can I deploy an LLM on the robot?
The platform supports development workflows for local or cloud-connected large-language-model integration. Practical model size and real-time performance depend on the selected Orin computer and software stack.
Does the robot support autonomous exploration and Nav2?
Yes. Development resources include RRT autonomous exploration, Nav2 path recording and playback, DWB / MPPI / RPP controller examples, coverage planning, and other navigation workflows.
Does the LSR300 support automatic recharging?
The LSR300 Series supports automatic recharging. Actual docking behavior depends on the installed charging hardware, navigation setup, and calibration.
Can the platform be used for outdoor 3D mapping or autonomous-driving research?
Yes. Select a C16 or C32 multi-line LiDAR when your project requires 3D point clouds, longer-range outdoor perception, mapping, or autonomous-driving research.
What source code and development materials are provided?
Development resources include chassis drive source code and schematics, ROS source code, 3D models, URDF resources, mapping and navigation examples, perception examples, and secondary-development materials.
Which model should I choose?
Choose LSR300 Mini for a smaller footprint and payloads up to 20 kg. Choose LSR300 Pro when you need a larger chassis and payload capacity up to 60 kg. Add C16 or C32 if your research requires multi-line 3D LiDAR.
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