<video><caption><image_4>The image shows a man standing in front of a green chalkboard with mathematical equations written on it. He is wearing a blue t-shirt with a logo on it and appears to be giving a presentation or lecture. The equations on the chalkboard are written in white chalk and are arranged in a grid-like pattern. The man is gesturing with his hands as if he is explaining something. He has a serious expression on his face and is looking directly at the camera.</caption><caption><image_5>The image shows a man standing in front of a green chalkboard with mathematical equations and formulas written on it. He is wearing a blue t-shirt with a logo on the left side of his chest. The man is smiling and appears to be giving a presentation or explaining something. The equations on the chalkboard are written in white chalk and there is a red circle on the bottom left corner of the image.</caption></video><transcript><audio_6>being model of how much translation occurred and that will get you pretty far that's better than just assuming that your eyeballs are individually just rotating in place right really it's your head that's turning um so so this um is a very helpful approximation</transcript><transcript><audio_7>to try to compensate for some of the position change but if the user decides to go back and forth like this and you might remember one of the motion cues called parallax where you see nearby objects moving at a faster rate than far away objects if you'd like to do these kinds of motions</transcript><caption><image_8>The image shows a man standing in front of a green chalkboard with various mathematical equations written on it. The man is wearing a blue t-shirt and appears to be in a classroom or a classroom setting. He is standing with his hands on his hips and has a surprised or shocked expression on his face. The equations on the chalkboard are written in white chalk and are arranged in a grid-like pattern. The background of the image is a whiteboard with a blue border.</caption><transcript><audio_9>and it doesn't work for you because you're not tracking the position back and forth then it ends up being uncomfortable so that causes motivation to bring in more sensors right so if you want to estimate position what more do we need is the question in addition to</transcript><transcript><audio_10>orientation again you can fake some of the position you might look at this this is the greatest hope so if you've just studied purely in textbooks you may say well i figured out how to separate up from linear acceleration of the body why</transcript><caption><image_11>The image shows a man standing in front of a green chalkboard with mathematical equations written on it. He is wearing a blue t-shirt and appears to be giving a presentation or lecture. The equations are written in white chalk and are arranged in a grid-like pattern. The man is standing with his hands in his pockets and is looking directly at the camera with a serious expression on his face. The background of the image is a classroom with a whiteboard and a chalkboard.</caption><transcript><audio_12>don't i just take this linear acceleration component and instead of throwing it away why don't i just integrate it once i get velocity integrate it twice and i get position so i can just figure out what your position is from that right</transcript><caption><image_13>The image shows a young man standing in front of a green chalkboard. He is wearing a blue t-shirt and has short dark hair. He has a big smile on his face and is gesturing with his right hand as if he is explaining something. On the chalkboard behind him, there are various mathematical equations and formulas written in white chalk. The equations appear to be related to physics or mathematics.</caption><caption><image_14>The image shows a man standing in front of a green chalkboard with mathematical equations written on it. He is wearing a blue t-shirt and appears to be in a classroom or a lecture hall. The equations are written in white chalk and are arranged in a grid-like pattern. The man is gesturing with his right hand as he speaks. He seems to be giving a presentation or explaining something to the audience.</caption><transcript><audio_15>let me explain some difficulties with that and this will motivate why we might one might want to use cameras and other more sophisticated sensors so if we just take this residual acceleration here left over after we got rid of the up component first of all we might have separated these two</transcript><transcript><audio_16>incorrectly another thing that might happen is um remember that we're always trying to estimate the orientation so if we have some errors in the estimation of the orientation then we will not know exactly which way up is when we do the</transcript><caption><image_17>The image shows a man standing in front of a green chalkboard with various mathematical equations written on it. He is wearing a blue t-shirt and appears to be giving a presentation or lecture. The equations are written in white chalk and are arranged in a grid-like pattern. The man is gesturing with his right hand as if he is explaining something. He has a serious expression on his face and is looking directly at the camera.</caption><transcript><audio_18>subtraction of up to get this residual component so that will lead to some additional error that doesn't look like noise it just looks like an offset because we don't know truly which way up is we cannot measure the we cannot estimate the global frame perfectly so that's going to cause additional errors</transcript> being model of how much translation occurred and that will get you pretty far that's better than just assuming that your eyeballs are individually just rotating in place right really it's your head that's turning um so so this um is a very helpful approximation to try to compensate for some of the position change but if the user decides to go back and forth like this and you might remember one of the motion cues called parallax where you see nearby objects moving at a faster rate than far away objects if you'd like to do these kinds of motions and it doesn't work for you because you're not tracking the position back and forth then it ends up being uncomfortable so that causes motivation to bring in more sensors right so if you want to estimate position what more do we need is the question in addition to orientation again you can fake some of the position you might look at this this is the greatest hope so if you've just studied purely in textbooks you may say well i figured out how to separate up from linear acceleration of the body why don't i just take this linear acceleration component and instead of throwing it away why don't i just integrate it once i get velocity integrate it twice and i get position so i can just figure out what your position is from that right let me explain some difficulties with that and this will motivate why we might one might want to use cameras and other more sophisticated sensors so if we just take this residual acceleration here left over after we got rid of the up component first of all we might have separated these two incorrectly another thing that might happen is um remember that we're always trying to estimate the orientation so if we have some errors in the estimation of the orientation then we will not know exactly which way up is when we do the subtraction of up to get this residual component so that will lead to some additional error that doesn't look like noise it just looks like an offset because we don't know truly which way up is we cannot measure the we cannot estimate the global frame perfectly so that's going to cause additional errors
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