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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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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Localization by comparing current lidar scan with previous lidar scan

I have managed to use an ICP algorithm to produce a relative pose difference between a new lidar range scan and the previous lidar scan. When I tested it on individual scan pairs, the results look ...
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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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How is loop closure mechanism incoporrated in particle filter based SLAM algorithm?

Could any one please explain, how is loop clousre mechanism introduced in filtering based SLAM techniques like FAST Slam ?
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Include information on the environment in map-aware particle filter

I have a robot equipped with some sensors for estimating the movement in a 2D environment (IMU, odometer). The robot is free to move within an area delimited by some walls. The map of the environment (...
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Particle filter on more dimensions?

I decided to write and implement a very small radar tracking program in order to understand the basics of the particle filter. So I wrote a class (class Aircraft) for a ideal moving plane which moves ...
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How to calculate the covariance and gain in SLAM when only one measurement is available?

I am trying to perform SLAM for cases where only one sensor measurement is available. For example, suppose I want to track the position of a robot moving in a room with multiple known landmarks (2D ...
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2 answers
37 views

Why does the complexity of the particle filter scales exponentially with the number of dimentions?

In the AI for Robotics course from Udacity, Sebastian Thrun mentions that "the complexity of the particle filter grows exponentially with the number of dimensions". Why is this the case? We ...
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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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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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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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2 votes
1 answer
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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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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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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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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 ...
3 votes
2 answers
125 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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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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1 answer
108 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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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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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 ...
2 votes
2 answers
63 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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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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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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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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4 votes
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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 ...
1 vote
1 answer
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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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4 votes
2 answers
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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 ...
4 votes
2 answers
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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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1 answer
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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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3 votes
1 answer
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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 ...
1 vote
2 answers
361 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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1 vote
1 answer
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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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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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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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1 vote
1 answer
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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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1 answer
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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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1 answer
993 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 ...
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2 votes
2 answers
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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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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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1 answer
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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. ...
4 votes
3 answers
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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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2 votes
1 answer
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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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4 votes
1 answer
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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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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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3 votes
3 answers
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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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3 answers
384 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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1 answer
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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 ...
1 vote
1 answer
252 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 ...
8 votes
2 answers
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 ...
5 votes
2 answers
476 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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