With SLAMcore, affordable spatial AI is finally within reach.
SLAMcore is making spatial AI solutions more accessible to robotics businesses, transforming their vision into a tangible reality
Underpinned by research from Imperial College London, SLAMcore isn’t just creating better SLAM solutions. Our team of leading academics, roboticists, engineers and developers are creating the future of it. Pushing the boundaries of what’s possible today and what SLAM will look like tomorrow.
We’re making spatial AI solutions more accessible to businesses by unlocking huge market opportunities in robotics with robust SLAM algorithms that can survive in the real world. By bridging the divide between demo videos and real-world performance, we are transforming the vision of our customers into a tangible reality.
SLAMcore is a pioneer in:
- Sparse feature-based SLAM for robots and drones
- Dense SLAM systems using both monochrome and/or RGB-D cameras
- High quality, tightly coupled visual-inertial fusion
- Semantic mapping
- Deep Learning for SLAM
Spatial AI: offering advanced SLAM solutions that can help a robot accurately understand where it is and what’s around it
With spatial AI, your robot or drone will be better able to understand the world around them. From humans to pets and objects they can interact with
Existing single sensor solutions limit a robot or drone’s capabilities. Fusing information from multiple sensors will provide greater detail on surroundings, location, and position. SLAMcore’s algorithms seamlessly combine data from a range of sensors to create this detailed view. With this information, robots and drones will perform better in various environments, with fewer blind spots, greater accuracy and more robustness.
Spatial AI – Deep Learning for SLAM
Deep learning that combines best-in-class techniques with original methods
SLAMcore is using deep learning to help robots and drones identify the things around them, helping you create robots that can work effectively in different environments. A factory robot, for example, will be able to differentiate between static equipment, human workers and vehicles. Enabling it to move around obstacles, avoid dangers, and locate itself in the factory.
At SLAMcore, our approach to deep learning combines best-in-class traditional techniques with original methods.
Our expertise includes:
- 3D Object segmentation and identification
- Determining depth from video
- SLAM in dynamic environments