Range sensors evaluation under smoky conditions for robotics applications

Abstract

In the paper we present performance reviews of some of the most popular range sensors in robotics under smoky conditions. The experiments were conducted in the same environment with the same source of smoke of different densities. The range sensors were setup to produce 3D point clouds. To evaluate the performance of the sensors two metrics were used: the total number of valid points produced by the 3D sensors and the number of points which could be extracted as fitting to a planar surface, both in respect to the smoke density

A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM

“…The main conclusion obtained in their benchmarking experiments was that all the LRFs tested provide different levels of noisy and erroneous results with saturated outputs, which makes them almost unusable under these conditions. Similar conclusions were obtained by Tretyakov and Linder [21], and in a recent comparison presented in Pomerleau et al [22], in which the LRF Hokuyo URG-04LX, also used in the results reported in section 5.4.1, presents the highest values of disparity and error in depth measurements, among of all compared LRFs. As distinct to previously described works, we herein propose a multi-sensor approach based on a LRF and a sonar array which, despite being based on an affordable setup using only commercial off-the-shelf (COTS) sensors, can provide a robust solution when LRF measures are partially disturbed by the presence of particles that reduce visibility.…”

When the Dust Settles: The Four Behaviors of LiDAR in the Presence of Fine Airborne Particulates

“…Difficulties arise because of the high variability of dust clouds, summarized in Figure , challenges associated in producing dust clouds of a defined character, and the difficulty of measuring/quantifying them. Knowledge of sensor performance in dust, or other particulates, from characterization has been used to aid sensor selection (Starr & Lattimer, ; Tretyakov & Linder, ), but to our knowledge, it has not been widely used to improve the algorithms that process LiDAR measurements into information. A gap in the study of the effect of dust on LiDAR is the experimental control of dust conditions.…”

“…Pascoal, Marques, & De Almeida () found that subjecting four LiDAR sensors (with wavelengths of 650–950 nm) to smoke and water vapor would either produce erroneous measurements on the front of the cloud (Behavior (B)), or saturate the sensor, producing no measurement at all (Behavior (D)). Formsma, Dijkshoorn, van Noort, & Visser () similarly found that smoke appears to LiDAR as a solid object, whereas Tretyakov & Linder () demonstrated that camera measurements cannot even be made under similar conditions. More recently, a comparative study by Starr & Lattimer () evaluated two IR cameras, two visible cameras, two sound navigation and ranging (SoNAR), a RaDAR, single and multiecho LiDAR, a Kinect, and a night‐vision sensor.…”

Probabilistic Model of Laser Range Finder for Three Dimensional Grid Cell in Close Range Environment

“…Another work that characterize the error of Hokuyo laser are by Park et al [11] who used UBG 04LX that follows the guidelines elucidated by Ye and Borenstein [5]. Tretyakov and Linden [12] evaluated the performance of UTM-30LX together with other scanners namely, SICK's LMS-291 and LMS-III, Swiss Ranger MESA SR-3100, and Microsoft Kinect under smoky condition. Tretyakov and Linden [12] validate the accuracy performance of the aforementioned scanners when introduced within a smoke laden room.…”