Questions tagged [particle-filter]

Particle filtering is a general Monte Carlo (sampling) method for performing inference in state-space models where the state of a system evolves in time and information about the state is obtained via noisy measurements made at each time step

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2answers
734 views

How to calculate probability of particle survival for particle filter?

I'm trying to figure out a way that I can calculate the probability that a particle will survive the re-sampling step in the particle filter algorithm. For the simple case of multinomial re-sampling, ...
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1answer
45 views

Vehicle Odometry Correction Using Lidar Contour Points (Localization)

I am currently working on a project where... Vehicle travels from point A to B GPS trajectory is used as a reference Vehicle odometry (bicycle model without slip angle beta) was used to estimate a ...
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18 views

Particle filter: do I always need for every system a dynamic model system?

I have a very basic question about particle filters and their applications. I know Kalman filters and I have implemented some of them. Taking a linear and unimodal Kalman filter, then I need a ...
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1answer
110 views

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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1answer
19 views

Doubt regarding the likelihood field in measurement model

I don't quite understand what xztk and yztk mean? Are they meant to represent the position of the kth laserscan reading? If yes, could someone please help me figure ...
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1answer
76 views

What should be the prediction step in particle filter?

I am implementing a particle filter using MATLAB. I am implementing it first time. I have written the system model and measurement model. Given below: ...
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2answers
85 views

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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2answers
85 views

Collaboration of mobile robot and survaillance camera - classic localization (still) needed?

Just started with the topic of mobile robotics.. so I'm still into concept making and little programming, but have not setup everything or tested at all. I got a (differential) mobile robot (lego ...
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2answers
262 views

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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0answers
154 views

Nonlinear Sensor Fusion with Space-Time Finite Element and Static Condensation?

I have recently implemented an algorithm for the nonlinear fusion of GNSS, barometer, magnetometer, accelerometer and gyroscope data. The algorithm is based on a space-time finite element where the ...
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21 views

difference between particle filter in slam and normal particle filter

I would like to ask you about what is the difference between particle filter when we use it in Slam methods and when we use it normally to correct our sensor measurement. I mean, is there any ...
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1answer
48 views

How do Particle Filters give estimates of uncertainty?

In the Kalman Filter the final covariance matrix is the estimate of the filter's uncertainty. How does one do so in Particle filters? Is it just the variance among the particles for each state? If so, ...
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1answer
37 views

2D point cloud registration success probability

I am trying to implement localisation by storing images from a camera and their accompanying point clouds from a 2D lidar during mapping. During localisation I then use image matching to suggest the ...
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1answer
79 views

How to generate Particles in Particle Filter?

I have been trying to implement FastSlam 1.0. To implement this, I need to create particles. Now my confusion is how to create Particles? I have some odometry and Measurement data. Using those data ...
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1answer
100 views

How does a clustered particle filter work?

Suppose we want to track multiple objects (robots, roads, people...) using clustered particle filtering (because we don't have an idea about how many objects there'll be, and the number of these may ...
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2answers
70 views

How to take prediction step in particle filter?

I am working on particle filter. I have studied it thoroughly, but stucked at one point during implementation. I have to implement it using MatLab. The issue is this that I am unable to implement the ...
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1answer
93 views

Robot localization in a known map without knowing its initial position in that map

Firstly I would like to say that I'm no expert in Bayesian Filters such as Kalman Filter and Particle Filter, but I've used the EKF before in a robot that has both wheel encoders and an IMU to ...
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2answers
53 views

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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0answers
53 views

Particle filter with error states

I am new to particle filters and I have a particle filter based on this: $$x_{k+1} = f_{k}(x_{k},\omega_{k})$$ $$y_{k+1} = h_{k}(x_{k},v_{k})$$ $$x = [\delta \phi, \delta \theta, \delta \psi, \delta ...
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115 views

Sensor model and Inverse sensor model using occupancy grid mapping with lidar for particle filter

I'm a bit confused about how one goes about calculating the sensor model $p(z_t|x_t, m) $ and inverse sensor model for position $p(x_t |z_t,m_{t-1})$. From this answer, it seems like one way for ...
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1answer
57 views

Help with Probabilistic Robotics Equation 13.22 detailed derivation

Equation 13.22 from Probabilistic Robotics below: Here's how I get from first line to second line: $$ p(x_{1:t}^{[k]} | z_{1:t},u_{1:t}, c_{1:t}) = \frac{p(x_{1:t}^{[k]}, z_{1:t},u_{1:t}c_{1:t}) }{ ...
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1answer
251 views

Likelihood Field Matching

I have LIDAR data of an environment in my hand and I want to apply likelihood field matching to this data. I found a source. But I don't understand what the variables of the algorithm mean and how it ...
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1answer
94 views

Fusing absolute robot localization from markers

I have a system which is composed of a rig of 8 cameras which are used for detecting markers in the environment and which outputs 8 estimates of the absolute robot's position and orientation. Now, I ...
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2answers
749 views

How does fast slam creates grid maps?

I've implemented fast slam using landmark detection and the map stored is a feature map, made of landmarks positions. I would like to create a grid map, and my questions are about how does the robot ...
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2answers
2k views

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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2answers
318 views

Laser Scanner for localization in particle filter

I'm working on a particle filter implementation in Matlab and I found one very good in the Robotics Toolbox (available here https://github.com/petercorke/robotics-toolbox-matlab). My problem is that I ...
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1answer
74 views

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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0answers
53 views

Why to stop a particle filter if the robot does not move?

It is clear it is not a good idea to do resampling in a particle filter if the robot is not moving (there is no action), as the particles will converge towards a simple particle. However, ...
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5answers
27k views

Particle filters: How to do resampling?

I understand the basic principle of a particle filter and tried to implement one. However, I got hung up on the resampling part. Theoretically speaking, it is quite simple: From the old (and ...
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0answers
41 views

Resampling step for MC variation reduction

I am reading particle filtering for robot localisation and specifically the resampling step to avoid particle degeneracy. Can anyone explain me what MC (Monte Carlo) variation means? I saw it couple ...
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1answer
94 views

How to track multiple robots with particle filter

I am using an IR camera to track N mobile robots driving about on the floor. Each robot has a few IR LEDs on its head in known locations, all at the same height above the floor. Each robot has 5 ...
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2answers
7k views

Difference between Rao-Blackwellized particle filters and regular ones

From what I've read so far, it seems that a Rao-Blackwellized particle filter is just a normal particle filter used after marginalizing a variable from: $$p(r_t,s_t | y^t)$$ I'm not really sure ...
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1answer
836 views

How to make a particle filter evaluation function with LIDAR sensing?

I am currently trying to implement a particle filter an a robot in a view to localize it on a 2D plane (i.e. to determine x, y ...
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3answers
567 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
893 views

How to implement a particle filter when sensors can't identify landmarks?

I'm attempting to build a robot that leverages a particle filter to identify where it is relative to a map that is known. The robot only has IR sensors, so while it is able to determine its distance ...
4
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1answer
479 views

Localization of a Robot to find it Coordinates according to the Known Map

I am a third-year electrical engineering student and am working on an intelligent autonomous robot in my summer vacations. The robot I am trying to make is supposed to be used in rescue operations. ...
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204 views

Introducing new particles in particle filters for localization

Standard particle filters can produce bad localization result if the initial particle generation step produces no particle that is close to location (and bearing) of tracked object. The accuracy ...
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1answer
135 views

How to implement Bounded Angle Vision in Particle Filter?

I have built a Particles Filter simulator and I wanted to add the following functionalities. Limited Range Vision (Robot can see up to 50 meters) Limited Angle Vision (Robot can see within a certain ...
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3answers
3k views

Particle filter weight function

I am trying to implement a particle filter in MATLAB to filter a robot's movement in 2D but I'm stuck at the weight function. My robot is detected by a camera via two points, so a single measure is a ...
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1answer
285 views

How to use a POMDP-based planner on top of a probabilistic filter

POMDPs extend MDPs by conceiling state and adding an observation model. A POMDP controller processes either action/observation histories or a bayesian belief state, computed from the observations (...
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1answer
3k views

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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3answers
1k views

Motion Model for Holonomic Robot

We are working with an holonomic robot equipped with three (120 degree shifted) omnidirectional wheels. The relative movement is estimated by dead reckoning using wheel encoders. To improve this ...
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2answers
300 views

Particle Filter Sampling Step

I emphasize that my question is about sampling, not resampling. I'm reading the Probabilistic Robotics book by Thrun et al, Chapter 4 on Non-Parametric Filters. The section on Particle filters has ...
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3answers
373 views

Is the accuracy of estimated position in localization better than estimated position in SLAM?

We estimate position of robot in localization and SLAM. My intuition says we get better position estimation in localisation than in SLAM because we have better sensor model likelihoods in localization ...
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1answer
79 views

Is a simple range sensor described below sufficient to implement particle filter localization?

I am trying to implement a monte carlo localization/particle filter localization with a simple range sensor. The range sensor only sees in the direction the robot is heading and returns back any ...
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1answer
229 views

Particles not behaving correctly in the implementation of particle filter

I am implementing a particle filter in Java. The problem with my particle filter implementation is that the particles suddenly go away from the robot i.e the resampling process is choosing particles ...
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2answers
1k views

Whats the logic to implement a particle filter for a robot with range sensor?

I am trying to implement a particle filter for a robot in Java. This robot is having a range sensor. The world has 6 obstacles - 3 in the top and 3 in bottom. I am calculating the distance of the ...
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1answer
2k views

What is the best way to fuse measurements from IMU, LIDAR, and Encoder information in some recursive bayesian filter?

I am doing SLAM with a four wheeled (2-wheel drive) differential drive robot driving through some hall way. The hallway is not flat everywhere. And the robot turns by spinning in place, then traveling ...
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2answers
385 views

Different Particle Filter min and max particle numbers give almost the same result

I'm using amcl package in ROS to localize a mobile robot. I've changed min_particles and max_particles several times then ...
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0answers
154 views

Random number generation for Particle Filter

I implemented a bootstrap Particle filter on C++ by reading few Papers and I first implemented a 1D mouse tracker which performed really well. I used normal Gaussian for weighting in this exam. I ...