eine neue runde eine neue reviewfahrt

tex_review/review/02_review.txt

@@ -20,9 +20,9 @@ Using a MCL you do not need to mix observations and actions in the same concept
 
 From my point of view formulation of transition model T should be tackled using actions (steps) and observations (s_wifi) should be used like an observation model V (described like "Evaluation" in section 5).
 
--> Within this work we do not incorporate a smoothing step, as smoothing requires high computational power. The basic sequential Monte Carlo scheme (particle filter) is however the same as in all our previous works. As we have a pattern recognition background, we often refer to the CONDENSATION algorithm instead of equivalent methods such as the bootstrap particle filter. Despite the fact, that CONDENSATION is often used within the field of visual tracking, it is based on the same assumptions as bootstrap or MCL, and thus equivalent. All three are assuming that the proposal distribution (sometimes called importance distribution), of the general sampling importance resampling (SIR) particle filter, is the state transition p(q_t | q_t-1). In order to incorporate previous observations, we extend the transition to p(q_t | q_t-1, o_t-1), incorporating previous observations. The validity of this statement can be easily proven. 
-We are not sure, what you exactly mean by "researchers do not access to the agent information". At the end, sensor data as well as preprocessed data (e.g. the activity) can be seen as part of the observation. The MCL you are referring to, is highly adapted to control theory and thus robot motion. However, we maintain a more probabilistic view of particle filtering, allowing for a more general formulation. Of course, it would also be possible to introduce some control (actions) command, as for example Sebastian Thrun did in his work "Probabilistic Robotics". However, this results in the same probability density, as the control (actions) would have the same data as the observation mentioned earlier. We highly appreciate your suggestion of reformulating the particle filtering, despite that we refrain from pursuing it. Finally, we do not use any out-of-the-box implementation from OpenCV or matlab. As stated at the beginning of our experiments, we developed a C++ backend for localization, running on both desktop and smartphone. 
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+-> Within this work we do not incorporate a smoothing step, as smoothing requires high computational power. The basic sequential Monte Carlo scheme (particle filter) is however the same as in all our previous works. As we have a pattern recognition background, we often refer to the CONDENSATION algorithm instead of equivalent methods such as the bootstrap particle filter. Despite the fact, that CONDENSATION is often used within the field of visual tracking, it is based on the same assumptions as bootstrap or MCL, and thus equivalent. All three are assuming that the proposal distribution (sometimes called importance distribution), of the general sampling importance resampling (SIR) particle filter, is the state transition p(q_t | q_t-1). In order to incorporate previous observations, we extend the transition to p(q_t | q_t-1, o_t-1), incorporating previous observations. The validity of this statement can be easily proven. For us, a particle filter algorithm is thus not defined by the area in which it is used, but by its statistical properties (e.g. the choice of the proposal or by using auxiliary variables).  
+We are not sure, what you exactly mean by "researchers do not access to the agent information". At the end, sensor data as well as preprocessed data (e.g. the activity) can be seen as part of the observation. The MCL you are referring to, is highly adapted to control theory and thus robot motion. However, we maintain a more probabilistic view of particle filtering, allowing for a more general formulation. Of course, it would also be possible to introduce some control (actions) command, as for example Sebastian Thrun did in his work "Probabilistic Robotics". However, this results in the same probability density, as the control (actions) would have the same data as the observation mentioned earlier. Again, we have chosen the filtering methodology because of its statistical properties and the general formulation, not by application. 
+We highly appreciate your suggestion of reformulating the particle filtering, despite that we refrain from pursuing it. Finally, we do not use any out-of-the-box implementation from OpenCV or matlab. As stated at the beginning of our experiments, we developed a C++ backend for localization, running on both desktop and smartphone. 
 
 
 line 226: What is z_t? Is an observation o_t? nomenclature should be unified through the whole paper