A route is a plan.
Feedback keeps checking.

Observe. Compare. Correct. Repeat.

Imagine an inspection robot moving along a corridor. Repeating the same motor command will not reveal that it has drifted. A camera can compare visible landmarks with a reference, estimate the displacement and request a correction. The next observation checks whether the correction helped.

Each stage can fail differently. A dark image may hide landmarks. Repeated patterns can produce the wrong match. A good estimate can arrive late. A sensible correction may exceed what the wheels or steering can deliver. Real visual navigation combines calibration, tracking, mapping and failure handling; ORB-SLAM is one documented example. The small models on this page do not implement it.

What the existing route calculation actually tested

A separate, older calculation compares lateral correction with no correction in an artificial corridor. It generates 64-bit feature descriptions and matches them using Hamming distance: the number of differing bits. At least six accepted correspondences are required for a lateral estimate.

The simulator supplies forward position in 0.1 m increments. It does not visually estimate forward travel, form real camera images or model physical motion over time. A nominal 200-step sequence has its final observation at 19.9 m. Random feature deletion imitates missing inputs, not the geometry of a real object blocking a camera.

The correction-enabled counts were recalculated from the 100 stored trial summaries. Its event flags were counted, but cannot be reconstructed without the missing step-by-step trajectories. The no-correction values are copied from the archived summary; its individual trial records are unavailable and it was not independently rerun. The comparison is internal to one synthetic simulator.

The median of the 100 trial median absolute errors is about 0.001582 m with correction. This is not the worst error, a pooled median of every update or a physical measurement precision.

An endpoint inside the chosen 0.25 m lateral band is not an arrival at a collision-free destination. A “recovery” is merely at least one sample inside the band in a subsequent window of up to 100 steps. It does not require sustained stability or even a preceding error outside that band. Some windows overlap, so 300 windows are not 300 independent trials.

The new gain-and-delay demonstration above is a different model. Its curves are not reconstructed traces of the older feature matcher.

A mobile camera is a sensing system

A camera's orientation, calibration, exposure and motion blur affect what an image means. Timestamps connect the image to the platform's position. A microphone also records motor noise, wind and vibration; recording more clearly is a different task from estimating position.

Processing, storage, power and communication all constrain the sensing chain. Sending an image elsewhere does not make it current: the robot may have moved before the answer returns. A received packet is not proof of a timely observation, and a command is not proof that the platform followed it.

Near a wall, there is less room for error

This section describes a real-world design concern; the page models do not include walls, ground clearance or collision avoidance. A route estimate cannot reveal every new obstacle. Close to a wall or the ground, sensing, clearance, reaction time and the ability to slow or stop must be evaluated together. Network coordination cannot replace a clearly defined local response when communication or sensing fails.

Nav2's Collision Monitor illustrates a separate software layer that responds to current nearby sensor information. Such software does not turn this page's tracking model into a certified safety system. A claim about accident probability would require a definition of an accident, an exposure denominator and independent evidence. No flight or passenger-safety result is established here.

What this helps us do

Today: robots inspect equipment, transport materials and construct maps in suitable operating environments. Looking at each stage of the feedback loop helps diagnose whether a failure began with poor observations, a wrong estimate, a delayed command or insufficient control authority.

A future direction: combine better visual estimates with explicit handling of stale or missing observations. Test changing lighting, repeated landmarks and communication failures against an independent position reference. Improvement needs to hold beyond the scenario used to tune the controller.