ROBOTICS & AVs
Navigation Edge Cases
Navigation Edge Cases. What it takes to run it.
Identifying and labeling corner cases for path planning algorithms.
// 01 · The question
“What is the situation the planner has not been trained for?”
// 02 · Why it is hard
By definition these are rare, so they cannot be collected by sampling. They have to be mined out of fleet footage by looking for the model's own uncertainty, which is a pipeline problem before it is a labelling one.
// 03 · What the labels have to be
Semantic segmentation
Decided before the first frame
Edge cases are usually about scene structure rather than a listed object: an unmarked ramp, a surface that reflects, a boundary that is not where it appears. Dense labelling captures a scene that has no clean object list.
This is the decision that is expensive to reverse. The geometry has to match what the answer contains, and finding out it does not means labelling the set a second time.
// 04 · How we run it
Three parts of one loop, on this job.
LexAnnotate
Pixel-Perfect Data
Pixel-level semantic segmentation, LiDAR point cloud annotation, sensor fusion labeling, and temporal event tracking for complex perception tasks. Expert annotators, accountable for every label.
LexInsight
Spatial Intelligence
Build spatial understanding from flat labels. Map object relationships, track movement patterns, identify zero-day anomalies that standard training data misses.
LexAlert
Perception Alerts
Continuously validate perception performance in production. Detect when sensor drift or environmental changes degrade navigation. Route failures back for retraining before they cause incidents.
// 05 · What breaks it after launch
Every new deployment site is a fresh distribution of edge cases. The long tail that was solved at the first facility is a different long tail at the second, and the fleet accuracy average hides it.
It is not the only one that can get this use case, it is the one that usually gets it first. All ten conditions.
// 06 · In robotics
Dynamic Spatial Navigation
The Challenge
Flat bounding boxes failed to provide spatial context for autonomous material handling in shifting environments.
Our Execution
Deployed pixel-level semantic segmentation and temporal event tracking for high-fidelity spatial datasets.
The Result
Enabled autonomous systems to adapt to changing environments safely. Reduced manual overrides.
95%+
Navigation reliability in changing environments