NE-VSLAM Edge Acceleration Platform
Real-time Visual SLAM on resource-constrained edge devices. We accelerate Visual SLAM through two complementary methods: software optimization for specific CPU architectures, and selective FPGA-based hardware acceleration. This lets robots localize and map where GPS and human control are unavailable.
- RGB-D tracking on a Raspberry Pi 4
- 31 FPS
RGB-D tracking on a Raspberry Pi 4
- Faster than the tested baselines
- 2.1 ×
Faster than the tested baselines
- Trajectory error (ATE RMSE)
- 10.2 mm
Trajectory error (ATE RMSE)
We're looking for partners, collaborators, and early adopters.
Product overview
One platform, two deployment levels
NE-VSLAM ships in two editions that share a common software and ROS 2 interface. Start on the CPU you already have; move compute-intensive stages onto an FPGA when your latency or energy budget demands it.
NE-VSLAM Software Edition
Available nowArchitecture-specific software optimization for embedded CPUs, with no discrete GPU or FPGA required.
Target platform
- Raspberry Pi 4 Model B, 4–8 GB
- Embedded ARM-based platform
Current result
- Up to 31 FPS
- Approximately 2.1× faster than the tested Stella-VSLAM and ORB-SLAM2 configurations
- No discrete GPU or FPGA required
Optimization approach
- Architecture-specific software optimization for embedded CPUs
NE-VSLAM FPGA Edition
In developmentThe same SLAM application, with selected compute-intensive stages offloaded to FPGA fabric.
Target platform
- AMD Kria KV260
- or other preferred FPGAs
Architecture
- Embedded processor manages the SLAM application, robotics integration, and system control
- FPGA accelerates selected compute-intensive processing stages
- Common software and ROS 2 interface
Purpose
- Higher throughput
- More deterministic latency
- Better efficiency for fixed workloads
Application scenario
Drops into the robot you already have
The same NE-VSLAM interface supports two deployment levels. The CPU-optimized version targets affordable robots and prototypes, while the CPU–FPGA version targets systems with stricter performance or energy requirements.
RGB-D camera
Input frames taken from an RGB-D camera.
Raspberry Pi 4 or Kria KV260
NE-VSLAM runs tracking, mapping and pose estimation on-device.
UAV or UGV
Your robot consumes pose directly for navigation and control.
Autonomous mobile robots (AMR)
On-board localization and mapping for indoor robot fleets.
Automated guided vehicles (AGV)
Reliable navigation for warehouse and factory transport.
Industrial & surveillance drones
Low-power, low-latency perception for battery-powered UAVs.
Research & prototype platforms
Affordable on-board SLAM for university labs and early robot builds.
Benchmark data
TUM RGB-D VSLAM benchmark comparison
NE-VSLAM delivers 31.255 FPS at 10.243 mm ATE RMSE, 2.104× the frame rate of ORB-SLAM2 and Stella-VSLAM.
ORB-SLAM2 and Stella-VSLAM are effectively tied at 14.860 and 14.858 FPS (0.010% mean-latency difference).
These are Software Edition results: CPU-only, with no FPGA or GPU acceleration involved.
Tracking latency
Y-axis: tracking latency (ms/frame) | lower is better | dashed line = 30 FPS target
Throughput
Y-axis: effective throughput (FPS) | higher is better | dashed line = 30 FPS target
Trajectory accuracy
X-axis: ATE RMSE (mm) | thin line = range; thick line = mean ± SD
Speed–accuracy position
X-axis: effective throughput (FPS) | Y-axis: ATE RMSE (mm) | dashed line = 30 FPS target
View data as table
| System | Tracking latency | Throughput | ATE RMSE (mean ± SD) | ATE range |
|---|---|---|---|---|
| ORB-SLAM2 | 67.296 ms | 14.860 FPS | 9.781 ± 0.230 mm | 9.424–10.141 mm |
| Stella-VSLAM | 67.303 ms | 14.858 FPS | 10.249 ± 0.196 mm | 9.912–10.502 mm |
| NE-VSLAM | 31.995 ms | 31.255 FPS | 10.243 ± 0.144 mm | 9.955–10.407 mm |
ROS 2 interface
Publishes pose straight into your ROS 2 graph
NE-VSLAM runs as a standard ROS 2 node. It subscribes to your existing RGB-D camera topics and broadcasts the map frame, so integration is a launch file rather than a porting project.
Camera
NE-VSLAM device
User robot
Custom acceleration service
Visual SLAM may not be your bottleneck
If your system has a computation, latency, or energy-efficiency problem, we can help optimize it, even when Visual SLAM is not the part that hurts.
Software
C++ optimization for your target CPU, including profiling, memory tuning, SIMD, and multithreading.
Hardware
Custom FPGA acceleration for AI, perception, and robotics workloads when CPU optimization is not enough.
Tell us about your robot, current computing platform, and main performance challenge. We will recommend the most suitable optimization path.
Describe your workloadOur story
Why we exist
NeuroEdge is a semiconductor research group founded in 2024 by alumni of ITS Surabaya. We develop hardware acceleration for real-time edge AI in robotics and autonomous systems. Autonomous robots need reliable localization when GPS and human control are unavailable or limited. Without proper optimization, CPUs can be too slow, while GPUs can use too much power. Our solution combines CPU architecture-specific software optimization with FPGA SoC acceleration for selected stages of the processing pipeline. We aim to close the gap between the perception algorithms developed by researchers and the hardware needed to run them efficiently on-device.
Our mission
Make advanced AI and 3D-vision algorithms run faster, cooler and cheaper on the devices where decisions actually happen.
A multidisciplinary team specializing in embedded software, FPGA design, digital integrated circuits, electronics, and robotics.
Lab B202, ITS Surabaya, Indonesia · Founded 2024
Contact
Tell us what you're building
We're looking for partners, collaborators, and early adopters. Share your robot, current computing platform, and main performance challenge. We will recommend the most suitable optimization path.