= Menu

Using a Distance Sensor to Detect Deviations in a Planar Surface

Carter Sifferman, William Sun, Mohit Gupta, Michael Gleicher
IEEE Robotics and Automation Letters, Volume 9, Number 10, page 8515 - 8522 — 2024
Download the publication : Sifferman_RA_L_April_2024.pdf [9.4Mo]  
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.

Images and movies

 

BibTex references

@Article{SSGG24,
  author       = "Sifferman, Carter and Sun, William and Gupta, Mohit and Gleicher, Michael",
  title        = "Using a Distance Sensor to Detect Deviations in a Planar Surface",
  journal      = "IEEE Robotics and Automation Letters",
  number       = "10",
  volume       = "9",
  pages        = "8515 - 8522",
  year         = "2024",
  doi          = "https://doi.org/10.1109/LRA.2024.3445665",
  projecturl   = "https://cpsiff.github.io/using_a_distance_sensor/",
  url          = "http://graphics.cs.wisc.edu/Papers/2024/SSGG24"
}
 

Other publications in the database