Using a Distance Sensor to Detect Deviations in a Planar Surface

TL;DR

This paper presents a novel method using raw time-of-flight sensor data and Gaussian mixture models to detect deviations on planar surfaces, improving obstacle detection for mobile robots.

Contribution

It introduces a new approach that leverages raw sensor data and probabilistic modeling to identify surface deviations, outperforming traditional distance-based methods.

Findings

Raw data-based method outperforms baseline distance-only approaches.

Effective detection of various surface deviations across multiple scenarios.

Enables robust obstacle and cliff avoidance in robotic applications.

Abstract

We investigate methods for determining if a planar surface contains geometric deviations (e.g., protrusions, objects, divots, or cliffs) using only an instantaneous measurement from a miniature optical time-of-flight sensor. The key to our method is to utilize the entirety of information encoded in raw time-of-flight data captured by off-the-shelf distance sensors. We provide an analysis of the problem in which we identify the key ambiguity between geometry and surface photometrics. To overcome this challenging ambiguity, we fit a Gaussian mixture model to a small dataset of planar surface measurements. This model implicitly captures the expected geometry and distribution of photometrics of the planar surface and is used to identify measurements that are likely to contain deviations. We characterize our method on a variety of surfaces and planar deviations across a range of scenarios. We find that our method utilizing raw time-of-flight data outperforms baselines which use only derived distance estimates. We build an example application in which our method enables mobile robot obstacle and cliff avoidance over a wide field-of-view.