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Questions tagged [probability]

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Lost on SEIF Slam repeated update landmark

I am working on Sparse Extended Information Slam. I take the reference from Probabilistic Robotics, by Dr.Sebastian Thrun (Chapter 12,page 303). I have some doubt about the implementation of the ...
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2answers
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How should I understand sequential importance resampling in a particle filter?

Suppose I implement a particle filter with $n$ particles. This is a brief description of my understanding of a particle filter. For the first step, I throw out $n$ particles some distance from my ...
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2answers
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Why does the low variance resampling algorithm for particle filters work?

I am studying and coding particle filters and I am using the Low variance sampling algorithm suggested in the Probabilistic Robotics book. I understand the procedure for the algorithm. A random number ...
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1answer
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Why does a Bayesian Filter require random controls?

As stated in Probabalistic Robotics, the proof for correctness of a Bayesian Filter relies on the fact that $$p(x_{t-1}|z_{1:t-1},\ u_{1:t}) = p(x_{t-1}|z_{1:t-1},\ u_{1:t-1})$$ In order to justify ...
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1answer
126 views

How can we estimate the likelihood field for a particular scan in probabilistic terms?

I am trying to implement a scan matcher using Scan based sensor model but I cant figure out how to estimate likelihood for a particular scan. Is there any implementation available ? Would be thankful ...
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1answer
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$p(m|x_t, u_t, x_{t-1})$ What does Thrun mean with the “map probability”?

Different question from the last one since I still struggle with the concept. In his book "Probabilistic Robotics", Thrun has the following equation: (Context here) (5.49) $p(x_t|u_t,x_{t-1},m) = \...
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0answers
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Probabilistic Robotics: Map-based motion model [closed]

I asked the following question in math.stackexchange, but realized that this might be the more appropriate place to post it: How did Thrun derive the following formula: (Context here) I think that ...
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1answer
102 views

Markov Localization using control as an input

When using Hidden Markov Models in Global Localization problems on the prediction step there is a need to calculate the probability of robot's pose given the actions (control u, odometry): ...
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What is the best way to compute the probabilistic belief of a robot equipped with a vision sensor?

I am trying to implement 'belief space' planning for a robot that has a camera as its main sensor. Similar to SLAM, the robot has a map of 3D points, and it localizes by performing 2D-3D matching with ...
8
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1answer
236 views

Understanding and implementing belief space planning

I am currently working on state estimation/navigation for a system with multiple robots. As of now, what I have is each robot localizing itself with a Kalman filter, given vision based measurements. ...
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3answers
312 views

Addressing the sample impoverishment in particle filter

I have implemented a particle filter algorithm for the state estimation of a mobile robot. There are several external range sensors(transmitters) in the environment which gives information on the ...
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1answer
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Modeling a robot to find its position

The task of the robot is as follows. My robot should catch another robot in the arena, which is trying to escape. The exact position of that robot is sent to my robot at 5Hz. Other than that I can use ...
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1answer
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Low variance resampling algorithm for particle filter

For my particle filter, I decided to try using the low variance resampling algorithm as suggested in Probabilistic Robotics. The algorithm implements systematic resampling while still considering ...
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1answer
100 views

Grid mapping probability calculation algorithmic complexity

I have stumbled upon an equation (http://i.stack.imgur.com/hv64E.png), where the probability of an occupancy grid map cell is calculated. My teacher insists that it's possible to approximate the ...
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1answer
65 views

Non-markovian problems/approaches in robotics

As far as i can tell, the markov assumption is quite ubiquitous in probabilistic methods for robotics and i can see why. The notion that you can summarize all of your robot's previous poses with its ...
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0answers
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Probabilistic Velocity Obstacles

I have been working with the Velocity Obstacles concept. Recently, I came across a probabilistic extension of this and couldn't understand the inner workings. Source: Recursive Probabilistic ...
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1answer
95 views

Laser Beam based model probability in case of single particle

I am trying to calculate likelihood of laser scan($Z$) at give pose($x$) with known map ($m$) using beam based model $P\left(z_t|x_t,m \right)=\prod_{i=1}^{n}P'\left(z_i|x_t,m \right)$ My scan ...