Distance error correction for time-of-flight cameras

2017 ◽  
Author(s):  
Peter Fuersattel ◽  
Christian Schaller ◽  
Andreas Maier ◽  
Christian Riess
Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1156
Author(s):  
Eu-Tteum Baek ◽  
Hyung-Jeong Yang ◽  
Soo-Hyung Kim ◽  
Gueesang Lee ◽  
Hieyong Jeong

A distance map captured using a time-of-flight (ToF) depth sensor has fundamental problems, such as ambiguous depth information in shiny or dark surfaces, optical noise, and mismatched boundaries. Severe depth errors exist in shiny and dark surfaces owing to excess reflection and excess absorption of light, respectively. Dealing with this problem has been a challenge due to the inherent hardware limitations of ToF, which measures the distance using the number of reflected photons. This study proposes a distance error correction method using three ToF sensors, set to different integration times to address the ambiguity in depth information. First, the three ToF depth sensors are installed horizontally at different integration times to capture distance maps at different integration times. Given the amplitude maps and error regions are estimated based on the amount of light, the estimated error regions are refined by exploiting the accurate depth information from the neighboring depth sensors that use different integration times. Moreover, we propose a new optical noise reduction filter that considers the distribution of the depth information biased toward one side. Experimental results verified that the proposed method overcomes the drawbacks of ToF cameras and provides enhanced distance maps.


2018 ◽  
Vol 2018 (13) ◽  
pp. 464-1-464-6
Author(s):  
Yunseok Song ◽  
Yo-Sung Ho

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094237
Author(s):  
Yu He ◽  
Shengyong Chen

The developing time-of-flight (TOF) camera is an attractive device for the robot vision system to capture real-time three-dimensional (3D) images, but the sensor suffers from the limit of low resolution and precision of images. This article proposes an approach to automatic generation of an imaging model in the 3D space for error correction. Through observation data, an initial coarse model of the depth image can be obtained for each TOF camera. Then, its accuracy is improved by an optimization method. Experiments are carried out using three TOF cameras. Results show that the accuracy is dramatically improved by the spatial correction model.


2014 ◽  
Vol 53 (7) ◽  
pp. 073104 ◽  
Author(s):  
Johannes Seiter ◽  
Michael Hofbauer ◽  
Milos Davidovic ◽  
Horst Zimmermann

2013 ◽  
Vol 303-306 ◽  
pp. 143-148
Author(s):  
Cheng Bo Yu ◽  
Lei Yu ◽  
Jun Tan ◽  
Rui Li ◽  
Dan Xiao ◽  
...  

Because of the DV-Hop algorithm has a big error in the estimation of the average hop distance, this paper proposed a weighted hop distance correction localization algorithm. The improved algorithm is carried out by introducing the average hop distance error correction value of the weighting processing, thereby reducing the hop distance error, and avoid the accumulation of errors in the subsequent computation process. The simulation results show that the improved DV-Hop algorithm reduces localization error effectively and has good stability without additional devices; therefore, it is a practical localization solution for WSN.


Metabolomics ◽  
2008 ◽  
Vol 4 (2) ◽  
pp. 171-182 ◽  
Author(s):  
Velitchka V. Mihaleva ◽  
Oscar Vorst ◽  
Chris Maliepaard ◽  
Harrie A. Verhoeven ◽  
Ric C. H. de Vos ◽  
...  

2017 ◽  
Vol 98 ◽  
pp. 56-61 ◽  
Author(s):  
Jiantai Dou ◽  
Zhishan Gao ◽  
Jun Ma ◽  
Caojin Yuan ◽  
Zhongming Yang ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document