Niko's Project Corner

Real-time interest point tracking

As men­tioned in an other ar­ti­cle about om­ni­di­rec­tional cam­eras, my Mas­ter's The­sis' main topic was real-time in­ter­est point ex­trac­tion and track­ing on an om­ni­di­rec­tional im­age in a chal­leng­ing forest en­vi­ron­ment. I found OpenCV's rou­ti­nes mostly rather slow and run­ning in a sin­gle thread, so I ended up im­ple­ment­ing ev­ery­thing my­self to gain more con­trol on the data flow and threads' de­pen­den­cies. The im­ple­mented code would si­mul­ta­ne­ously use 4 threads on CPU and a few hun­dred on the GPU, ex­ecut­ing in­ter­est point ex­trac­tion and match­ing at 27 fps (37 ms/frame) for 1800 × 360 pix­els (≈0.65 Mpix) panoramic im­age.

Rendering omnidirectional images

As I men­tioned in the pre­vi­ous ar­ti­cle about om­ni­di­rec­tional cam­eras, my Mas­ters of Sci­ence The­sis in­volved the us­age of this spe­cial kind of imag­ing sys­tem which con­sists of a tra­di­tional cam­era lens and a con­cave mir­ror, which pro­vided 360° × 90° Field of View. It was or­dered from Japan and there was some de­lay in the de­liv­ery, so mean­while I wrote an all-Mat­lab script to sim­ulate this sys­tem's prop­er­ties, cal­ibra­tion and panorama gen­er­ation in prac­tice.

Omnidirectional cameras

My Mas­ters of Sci­ence The­sis in­volved the us­age of a so-called "om­ni­di­rec­tional cam­era". There are vari­ous ways of achiev­ing 180° or even 360° view, with their dis­tinct pros and cons. The gen­eral ben­efit of these al­ter­na­tive cam­era sys­tems is that ob­jects don't need to be tracked, be­cause gen­er­ally they stay with­ing the ex­tremely broad Field of View (FoV) of the cam­era. This is also very ben­efi­cial in vi­sual odom­etry tasks, be­cause land­marks can be tracked for longer pe­ri­ods of time.