PhD Position in Efficient Very-Wide-Area ToF 3D Sensing by Means of Adaptive Compressive Sensing

A question that naturally arises in active sensing systems, such as ToF systems, is how much volume can be sensed with a given power budget, and how this can be extended by means of some more intricate sensing scheme. The main objective of this project is the development of a very-wide-area ToF 3D sensing system which has to be outstandingly efficient regarding the power consumption. To attain such an ambitious goal we propose bringing compressive sensing into the game and using recently proposed adaptive methods for constructing close-to-optimal binary sensing matrices. Such matrices, initially developed at ZESS for use in pulsed ToF, are able to progressively concentrate the sensing power in those regions of the domain where the signals (e.g., sparse targets) live. This way the SNR of the measurements can be effectively improved by synthetically reducing the signal domain or, complementary, increasing the range of the system without increasing the power budget.

The successful candidate will be employed for a maximum period of three years full-time equivalent and receives a generous financial package plus an additional mobility and family allowance according to the rules for Early Stage Researchers (ESRs) in an EU Marie Sklodowska-Curie Actions Innovative Training Networks (ITN). A career development plan will be prepared for each fellow in accordance with his/her supervisor and will include training, planned secondments and outreach activities in partner institutions of the network. The ESR fellows are supposed to complete their PhD thesis by the end of the 3rd year of their employment. For more information please visit the Marie Sklodowska-Curie Actions Innovative Training Networks website.

YOUR TASKS

• Evaluation of the possibilities and main trade-offs of a very-wide-area ToF 3D sensing system.
• The advantages of implementing close-to-optimal binary sensing matrices for acquiring the measurements are to be evaluated. The performance improvement attained by means of adaptive construction methods is to be evaluated. Adaptation is first to be considered in depth domain and then extended to the full 3D space.
• Building upon the previous results, the ESR should select or develop his own tailored sensing strategies.
• For the selected sensing schemes, realistic requirements for the hardware to develop should be defined.
• A final hardware design is to be outlined and constructed, according to the selected sensing scheme and system requirements. The system has to be composed by a high power modular illumination system and a ToF camera able to use custom binary codes as control signals. The latter can be a solution provided either by CiTIUS or pmdtec. The performance of the prototype as 3D imaging system is to be evaluated, both in controlled scenes and real-world scenes, and contrasted to the initial simulation results. Different methods for reconstructing the (sparse) 3D scene are to be considered that exploit the structure in the sensing matrix and side information from adaptation.
• The performance of the prototype as 3D imaging system is to be evaluated, both in controlled scenes and real-world scenes, and contrasted to the initial simulation results. Different methods for reconstructing the (sparse) 3D scene are to be considered that exploit the structure in the sensing matrix and side information from adaptation.

PROFILE

• Master of Science in Electrical Engineering, Electronics Technology, Electrical Engineering Technology, Electrical and Computer Engineering, Physics, Industrial Engineering, Information Technologies or related fields
• Master of Science in Electrical Engineering, Electronics Technology, Electrical Engineering Technology, Electrical and Computer Engineering, Physics, Industrial Engineering, Information Technologies or related fields
• ESRs must demonstrate that their ability to understand and express themselves in both written and spoken English is sufficiently high for them to derive the full benefit from the network training. Non-native English speakers are required to provide evidence of English language competency (TOEFL, …).
• Knowledge and experience on scientific programming languages, e.g., Matlab, Python, C, C++, etc.
• Some basic knowledge on signal processing and Time-of-Flight imaging
• Knowledge on compressive sensing would be an advantage

PLANNED SECONDMENTS

• INSITU, Vigo, Spain, Prof. Dr. P. Arias Sánchez, 4 months, real-world performance evaluation of the prototype. Prospective applications. Testing in INSITU mobile platforms.

ADDITIONAL INFORMATION

Contact and further Information

https://www.uni-siegen.de/zess