Questions tagged [probability]
The probability tag has no usage guidance.
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How can I derive a metric comparing two 2D laser scans quantitatively, WITHOUT using any scan matching techniques?
TLDR version: I have two laser scans taken at the same pose that align nearly completely (see image). I want to come up with a metric that can quantify how well one did as opposed to the other.
Longer ...
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2
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Marginalization vs Dropping states for sliding window VO
When doing fixed lag smoothing or Windowed Smoothing for visual odometry and map construction, how does marginaliztion differ from dropping past states? How does each work?
I assumed fixed lag ...
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Sampling in non-parametric distribtution
I am currently working on a package in C++ to easily implement different Statistical Filters, and after implementing various versions of the Kalman Filter I decided to start working on the Particle ...
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Confused with EKF Localization
I have been trying to understand EKF localization from Probabilistic Robotics by Thrun Burgard and Fox.
There the covariance prediction is given by
$$\overline{\Sigma }_t=G_t\Sigma_{t-1}G^T_t+V_tM_{t-...
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Where do sensor models come from?
i'm new to robotics. I understood why we talk about probabilities and probabilistic robots, but i don't understand where those probabilities come from, how do i go about building a sensor model? Does ...
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Does the Bayes-Filter perform a convolution in the prediction step?
I am watching the (fantastic) SLAM lectures of Claus Brenner, where he introduces the Bayes-Filter (Kalman-Filter, Particle-Filter, Histogram-Filter).
He says, that the prediction step involves the ...
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Particle Filter for IMU tilt angle and bias estimation from Kalman Filter models
I understand the functioning of Particle Filters from the book Probabilistic Robotics and the robotics course provided by Cyrill Stachniss.
I want to implement, from scratch, a particle filter to ...
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Why Does an Exponential Make ANYTHING a Probability Distribution
I am posting this here because my background in estimation theory and optimization has been developed entirely through my experience in robotics.
TLDR: What makes it so that any time you put something ...
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Implementation of a particle filter algorithm
How do you calculate the likelihood of making an observation ?
Does someone have a link to a book or article that explains the math that you need to do to implement a PF algorithm?
Conceptually i ...
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Which math books would help in learning SLAM systems? [closed]
Recently I started studying papers on SLAM systems by Durrant-Whyte for my research and I'm finding some difficulties in the math (matrices and probability) that is tackled in these papers.
Which ...
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Basic question about Markov Localization, probability and belief distribution shift
I am starting in robotics, and reading about Markov Localization, I have one doubt, probably very stupid, but, well, I want to solve it.
Let's take the CMU Website example: https://www.cs.cmu.edu/afs/...
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Sampling from guassian distribution for odometry based motion?
I am trying to generate an odometry based motion model as described in the lectures. I have implemented a C++ version of the code. The issue I am having is that when I try to simulate successive ...
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How to derive the Time-Update equation of SLAM
I was going through the tutorial on SLAM by Hugh Durrant-Whyte and Tim Bailey† and found this Time-Update equation (equation 4 in the paper):
$
P\left(\mathrm{x}_{k},\mathrm{m} | \mathrm{Z}_{0:...
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How to calculate the mean of an unsymmetric distribution (Particle Filter)
I'm attempting to implement a variant of Monte Carlo localization in a 2D space with obstacles. While the object is moving around the obstacles the particles flow around the obstacle like in images ...
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Can neural network probability directly be used as inverse sensor model?
For my robot i'm using semantic segmentation neural network that assigns to every pixel probability of being "road" (not occupied). By using homography matrix i'm re-projecting image to top-down view. ...
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EKF SLAM : Missing older landmarks in new observation
I am a beginner to SLAM and robotics in general and I have been trying to implement SLAM on my GoPiGo3 robot car kit using primarily Chapter 10 from Probabilistic Robotics by Sebastian Thrun as ...
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Is Fuzzy logic applicable for robotics research
I found some papers that use fuzzy logic to solve robotics algorithmic problems. But I have recently been told by a few roboticists that fuzzy logic should not be used in robotics because it has ...
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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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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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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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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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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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$p(m|x_t, u_t, x_{t-1})$ 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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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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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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431
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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 ...
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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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3
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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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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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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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Grid mapping probability calculation algorithmic complexity
I have stumbled upon an equation (https://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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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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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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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 ...
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