[Sample Data] Step and Drop Detection (Sidewalk)
Ensure the safety of your autonomous robots with accurate step recognition. Experience high-precision
distance data designed for detecting sidewalk curbs and minor floor height differences.
Table of Contents
Sensing Technology for Solving Real-World Challenges
Reliable step recognition is a critical requirement for Autonomous Mobile Robots (AMRs) to navigate safely and efficiently. To determine whether a robot can safely traverse a bump or if a drop-off is within its limits, high-precision distance data is non-negotiable.
This Sample Data focuses on these navigation essentials. In this dataset, we captured a 20cm step, which modeled after the standard height of a Japanese sidewalk. Observe the exceptional clarity of the Accuracy even when capturing from a high angle down to the lower surface.
As a bonus, we have also included data for a subtle 3cm step. If detecting small obstacles or minor height
differences has been a challenge for your system, explore how senSPureTM maintains data integrity where other sensors fail.
What’s Inside the Dataset?
| Scenario | Details |
|---|---|
| Environment | Outdoor Sidewalk (Urban environment) |
| Target | – 20cm Sidewalk Curb (Standard in Japan) – 3cm Small Step |
| Illuminance | 15,000 lx (Standard outdoor daylight) |
| Camera Settings | Camera Height: 80cm / 30fps HDR mode |
| Viewing Angle | High-angle “Look-down” (Typical for AMR sensor mounting) |
| Technology Focus | Verification of stable Accuracy and point cloud integrity at drop-offs |