When you walk into a room you have never visited before, your visual system does something extraordinary in the first fraction of a second. It builds a three-dimensional map of the space, identifies surfaces, estimates distances, recognises objects, and begins predicting how you can move through it — all before you have consciously registered what you are looking at.
Getting a robot to do something remotely similar has occupied engineers and researchers for decades. The problem is not capturing light. A basic camera has been able to do that since the 1960s. The problem is making meaning from light — transforming raw sensor data into a structured understanding of space that a machine can act on.
"The camera is not the hard part. Understanding what the camera sees — that is the entire problem."
— Fei-Fei Li, Stanford Vision LabModern robotic vision systems solve this problem by stacking multiple sensing modalities on top of each other and fusing their outputs. No single sensor provides enough information on its own. The robot needs depth, colour, texture, motion, and semantic context simultaneously — and it needs all of them in real time.
The Sensor Stack
Most capable robotic vision systems in 2025 use at least three distinct sensing technologies working in concert. Understanding each one separately is the first step to understanding how they combine.
What is sensor fusion?
Sensor fusion is the process of combining data from multiple sensors to produce information that is more accurate or complete than any individual sensor could provide alone. In robotic vision, this typically means merging data from cameras, LiDAR, and inertial measurement units into a unified spatial model.
RGB cameras capture colour and texture with high resolution but provide no inherent depth information. A camera image is fundamentally flat — a two-dimensional projection of a three-dimensional world. Extracting depth from a single camera requires inference, which introduces error. Two cameras arranged in a stereo configuration can triangulate depth geometrically, similar to how human binocular vision works, but this method becomes unreliable beyond a few metres.
LiDAR — Light Detection and Ranging
LiDAR solves the depth problem directly. A LiDAR unit fires pulses of laser light and measures how long each pulse takes to return after bouncing off a surface. Because light travels at a known speed, this time measurement translates precisely into distance. A rotating LiDAR head can capture hundreds of thousands of such measurements per second, building a dense three-dimensional point cloud of everything in the sensor's field of view.
The weakness of LiDAR is that it captures geometry without colour or texture. A point cloud tells the robot precisely where surfaces are but nothing about what those surfaces are. A white wall and a white refrigerator are geometrically indistinguishable to LiDAR alone.
From Sensor Data to Semantic Understanding
Raw sensor data — whether from a camera or a LiDAR unit — is not yet useful to a robot trying to navigate or manipulate objects. The data must be processed through a pipeline that progressively extracts higher levels of meaning.
The first stage is localisation — the robot must establish where it is in space. This is more difficult than it sounds. GPS is unavailable indoors and unreliable in complex urban environments. Robots instead use a technique called Simultaneous Localisation and Mapping, or SLAM, which builds a map of the environment and tracks the robot's position within it at the same time — a problem that is elegantly circular in its difficulty.
The second stage is object detection and classification. Modern systems use convolutional neural networks trained on millions of labelled images to identify objects within camera frames. The same networks can now operate at real-time speeds on embedded hardware, enabling a robot to simultaneously recognise a door handle, a person, a chair, and a coffee cup within a single processed frame.
The third and most demanding stage is scene understanding — integrating all available information into a coherent model of what the environment is, what the objects in it are for, and how the robot should interact with it. This is where the field is moving fastest in 2025, driven by the application of large vision-language models to robotic perception.