https://github.com/permutationlock/slam_js

JavaScript library for Simultaneous Localization And Mapping. Focuses on dense occupancy grid mapping using particle fliters and distributed particle trees.
https://github.com/permutationlock/slam_js

dp-slam particle-filter robot sensor-models slam

Last synced: 2 days ago
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JavaScript library for Simultaneous Localization And Mapping. Focuses on dense occupancy grid mapping using particle fliters and distributed particle trees.

• Host: GitHub
• URL: https://github.com/permutationlock/slam_js
• Owner: permutationlock
• Created: 2016-08-11T13:06:52.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2019-06-04T20:42:49.000Z (over 5 years ago)
• Last Synced: 2023-03-02T00:35:37.550Z (over 1 year ago)
• Topics: dp-slam, particle-filter, robot, sensor-models, slam
• Language: JavaScript
• Size: 446 KB
• Stars: 20
• Watchers: 2
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# DP SLAM in JavaScript

 A JavaScript implementation of the basic

 [DP Slam](https://users.cs.duke.edu/~parr/dpslam/) algorithm.

 

## Features

### Modular Design

 A "modular" design that allows different map, motion, and sensor models. We

 first provide an implementation of a basic particle filter. We then use this

 particle filter to construct a generic implementation of the DP SLAM algorithm

 as a JavaScript class.

 

 Abstract concepts are described for map, motion, and sensor model objects. The

 DP SLAM object may then be instantiated with any parameters satisfying the

 necessary concepts.

 

 Concrete map, motion, and sensor model classes are provided that implement the

 ideas in the

 [DP SLAM 1.0 paper](http://people.ee.duke.edu/~lcarin/Lihan4.21.06a.pdf).

 

### Simulation

 A very basic simulation of a robot with a laser range sensor is provided in

 the main.html file. Walls may be added by clicking twice on the canvas. The

 simulation may be started and paused by pressing space. A particle is sampled

 from the DP SLAM particle filter at fixed time intervals. The grid shows what

 the current sampled particle predicts the environment to be. The green line shows the

 actual position and orientation of the robot. The blue line shows the

 what the current sampled particle predicts the robot's position and orientation

 to be.

### Funding

 This project was produced during an NSF funded research assistantship for the the

 ITEST research project (see [robotmoose.com](https://robotmoose.com/intro/)).