ENGINEERING
IN PRACTICE.

Machine perception increasingly runs at the platform edge rather than in a datacentre. Cameras and other sensors feed models that must produce reliable results within a fixed latency budget, on hardware with strict power and thermal limits.

Models that perform well on curated datasets degrade under motion blur, low light, occlusion and domain shift. Sustaining accuracy at real-time rates on embedded compute is the central constraint.

Perception is treated as one pipeline: dataset architecture, model design and training, optimisation for the target processor, embedded integration and on-target evaluation. Each stage is instrumented so regressions are caught against realistic data rather than benchmark sets.

COMPUTER VISION / MACHINE LEARNING / EDGE COMPUTE / REAL-TIME INFERENCE

Many operating environments — indoors, underground, at sea or under interference — deny reliable satellite positioning. Autonomous platforms must estimate their own state from what they sense.

Individual sensors drift, alias and fail. The task is fusing imperfect measurements into a stable, honest state estimate under real-time and compute constraints — and degrading gracefully when inputs are lost.

Vision-led odometry combined with inertial and auxiliary sensing through factor-graph estimation; map-relative localisation where prior structure exists; planning and control designed against the estimator's actual error characteristics; simulation used to rehearse failure cases before hardware.

ROBOTICS / NAVIGATION / SENSOR FUSION / STATE ESTIMATION

Models are typically trained in unconstrained compute environments. Deployment targets are embedded boards with fixed memory, power and thermal ceilings — and hard real-time expectations.

Reducing latency and memory by large factors while preserving task accuracy, and making execution timing deterministic enough to sit inside a control loop.

Architecture selection against the target processor, quantisation-aware training, compiler and runtime optimisation, scheduling and memory planning — then on-target profiling with regression gates so optimisation gains hold across releases.

EMBEDDED AI / OPTIMISATION / REAL-TIME SYSTEMS