Multi-Camera Vision-AI SLAM for Autonomous Robots and Vehicles

Autonomous robots and unmanned vehicles require precise positioning to navigate efficiently, yet many existing solutions highly depend on costly HD maps, 3D LiDAR, or GNSS/RTK signals. oToBrite’s innovative multi-camera vision-AI SLAM system—oToSLAM—provides a breakthrough alternative by ensuring reliable mapping and positioning in both indoor and outdoor environments without the need for external infrastructure.

Utilizing four automotive-grade cameras, an edge AI device (<10 TOPS), and advanced vision-AI technology, the system integrates key technology such as object classification, freespace segmentation, semantics and 3D feature mapping with optimized low-bit AI model quantization and pruning. This cost-effective yet high-performance solution achieves positioning accuracy of up to 1 cm (depending on the environment and use of additional sensors), outperforming conventional methods in both affordability and precision.

Figure 1: oToSLAM Multi-camera Vision-AI SLAM Positioning System.

When we talk about vision SLAM technology, the most common major challenge we encountered was the limitation of traditional CV-based SLAM. While this technology is computationally efficient, their accuracy and adaptability across diverse environments were insufficient for real-world deployment. In particular, CV-based approaches struggled in scenarios with low-texture scenes, dynamic objects, and varying lighting conditions, leading to degraded localization performance. After extensive testing and evaluation across multiple use cases, we ultimately chose to adopt vision-AI SLAM technology. By leveraging deep learning, we were able to extract more robust and meaningful 3D features, significantly improving positioning accuracy and environmental adaptability. This transition to AI-driven SLAM allowed us to build a solution that not only performs reliably in complex environments but also scales effectively for mass production and long-term maintenance.

Figure 2: CV-based 3D features vs. Vision-AI 3D features

In terms of hardware integration, selecting the most effective system configuration was also a critical task during the product development process. To strike the right balance between performance, cost, and feasibility, we carried out extensive testing and validation across different camera setups. Our analysis revealed that increasing the number of cameras significantly improves both environmental adaptability and localization accuracy. While 5-camera and 4-camera configurations deliver the best performance, 3-camera and 2-camera systems offer only moderate capability. In contrast, a single-camera setup proves to be considerably less effective. Based on these findings, we recommend a 4-camera configuration as the most practical choice—delivering robust, reliable SLAM performance suitable for real-world autonomous applications.

Figure 3: Our testing dataset covers indoor and outdoor scenarios, especially those with low-texture scenes, dynamic objects, and varying lighting conditions.

Figure 4: Comparison of SLAM Performance – Single Camera vs. Multi-camera Configurations

The next challenge we faced was selecting a cost-effective and accurate vision-AI model for vision SLAM 3D features extraction that could strike the right balance between performance, resource constraints, and real-world deployment feasibility. Since our solution targets mass production on a TI TDA4V MidEco 8 TOPS platform, the chosen model had to deliver precise localization without exceeding the system’s computational limits. While many AI-based models deliver excellent accuracy, they often require high computing power, which exceeds the capabilities of our target platforms. Therefore, we focused on lightweight architectures that maintain strong localization performance. After extensive evaluation, we chose one of the algorithms that offers a high localization success rate (>90%) and relatively low positioning error (<20cm) within our system constraints.

Figure 5: Computation Cost Estimation (only listing models with high localization success rate and relatively low positioning error)

However, implementing AI algorithms on the TI TDA4V MidEco 8-TOPS platform presented new challenges. The model processes images layer by layer to generate features, but not all layers are natively supported on the production platform. While standard layers such as CONV and RELU are compatible, others require custom development. To bridge this gap, we created additional algorithm packages to ensure compatibility and preserve model functionality while adapting it for real-world deployment.

Figure 6: Model Simplification and Adaptation

Another key challenge we faced during the transition to mass production was the limitation of relying solely on non-semantic feature points generated by the model. Although these 3D feature points are highly repeatable and robust across varying perspectives, they lack semantic context—such as identifying curbs, lane markings, walls, and other critical environmental structures. Through comprehensive analysis across diverse driving scenarios, we found that combining 3D non-semantic features and semantic feature points significantly improves the precision and robustness of our VSLAM system. This hybrid approach allows us to leverage the geometric stability of non-semantic features while enhancing environmental understanding through semantic context. As a result, integrating both feature types within the VSLAM pipeline have become a core strategy in overcoming the limitations of pure 3D point-based tracking. It plays a vital role in achieving higher accuracy, consistency, and resilience—especially in complex, dynamic environments—and serves as a key differentiator for our solution in the market.

Figure 7: oToSLAM using multi-camera vision-AI technology with semantic and 3D features, semantic features can cover various road markings as well as objects like vehicles, pillars, walls, curbs, wheel stoppers, etc.

Optimizing AI-based VSLAM models involves several challenges, including high computational complexity, difficulty in generalizing across diverse environments, and handling dynamic scenes. To overcome these, we adopt lightweight neural network architectures and quantization techniques for real-time performance on edge devices. Furthermore, we are not just optimizing the VSLAM models for 3D feature extraction, but also adding value with semantic features extraction via customized lightweight object classification and image segmentation. In the end, we enable multi-camera vision-AI SLAM from research to edge AI device mass production for autonomous robots and unmanned vehicles .

Learn more about oToSLAM: https://www.otobrite.com/product/otoslam-vision-ai-positioning-system

Appendix

Reference Models: 
The following models were referenced during the development process:

• ORB-SLAM: a Versatile and Accurate Monocular SLAM System 
• LIFT: Learned Invariant Feature Transform 
• SuperPoint: Self-Supervised Interest Point Detection and Description 
• GCNv2: Efficient Correspondence Prediction for Real-Time SLAM 
• R2D2: Repeatable and Reliable Detector and Descriptor 
• Use of a Weighted ICP Algorithm to Precisely Determine USV Movement Parameters 

Original Source:
This article was originally published on EE Times. Please refer to the original article here: https://www.eetimes.com/multi-camera-vision-ai-slam-for-autonomous-robots-and-vehicles/