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https://github.com/iris-ua/iris_lama

LaMa - A Localization and Mapping library
https://github.com/iris-ua/iris_lama

localization mapping robotics slam

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LaMa - A Localization and Mapping library

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README 

        
LaMa - A Localization and Mapping library.

==========================================

https://github.com/iris-ua/iris_lama



![Build](https://github.com/iris-ua/iris_lama/workflows/Build/badge.svg)



Developed and maintained by Eurico Pedrosa, University of Aveiro (C) 2019.



Overview

--------

LaMa is a C++11 software library for robotic localization and mapping developed at the **Intelligent Robotics and Systems** (IRIS) Laboratory from the University of Aveiro - Portugal. It includes a framework for 3D volumetric grids (for mapping), a localization algorithm based on scan matching and two SLAM solution (an *Online SLAM* and a *Particle Filter SLAM*).



The main feature is *efficiency*. Low computational effort and low memory usage whenever possible. The minimum viable computer to run our localization and SLAM solutions is a [Raspberry Pi 3 Model B+](https://www.raspberrypi.org/products/raspberry-pi-3-model-b-plus/).



#### Build



To build LaMa, clone it from GitHub and use CMake to build.

```

$ git clone https://github.com/iris-ua/iris_lama

$ cd iris_lama

$ mkdir build

$ cd build

$ cmake ..

```

Its only dependency is [Eigen3](http://eigen.tuxfamily.org).

**Note**: LaMa does not provide any executable. For an example on how to use it, please take a look at our integration with ROS.



#### Integration with ROS 



The source code contains `package.xml` so that it can be used as a library from external ros packages.

We provide ROS nodes to run the localization and the two SLAM solutions. Please go to [iris_lama_ros](https://github.com/iris-ua/iris_lama_ros) for more information.



Sparse-Dense Mapping (SDM)

--------------------------

Sparse-Dense Mapping (SDM) is a framework for efficient implementation of 3D volumetric grids. Its divides space into small dense *patches* addressable by a sparse data-structure. To improve memory usage each individual *patch* can be compressed during live operations using lossless data compression (currently [lz4](https://github.com/lz4/lz4) and [Zstandard](https://github.com/facebook/zstd)) with *low overhead*.

It can be a replacement for [OctoMap](https://octomap.github.io/).



Currently it has the following grid maps implemented:

* **Distance Map**: It provides the distance to the closest occupied cells in the map. We provide the `DynamicDistanceMap` which is an implementation of the dynamic Euclidean map proposed by:

    > B. Lau, C. Sprunk, and W. Burgard 

    > **Efficient Grid-Based Spatial Representations for Robot Navigation in Dynamic Environments**

    > Robotics and Autonomous Systems, 61 (10), 2013, pp. 1116-1130, Elsevier



* **Occupancy Map**: The most common representation of the environment used in robotics. Three (3) variants of the occupancy map are provided: a `SimpleOccupancyMap` where each cell has a tri-state: *free*, *occupied* or *unknown*: a `ProbabilisticOccupancyMap` that encodes the occupancy probability of each cell with logods; and a `FrequencyOccupancyMap` that tracks the number of times a beam hits or traverses (miss) a cell and calculates a hit/miss ratio.



For more information about **SDM** please read

> Eurico Pedrosa, Artur Pereira, Nuno Lau\

> **A Sparse-Dense Approach for Efficient Grid Mapping**\

> 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)



Localization based on Scan Matching

-----------------------------------

We provide a **fast** scan matching approach to mobile robot localization supported by a continuous likelihood field. It can be used to provide accurate localization for robots equipped with a laser and a *not so good* odometry. Nevertheless, a good odometry is always recommended.



> Eurico Pedrosa, Artur Pereira, Nuno Lau\

> **Efficient Localization Based on Scan Matching with a Continuous Likelihood Field**\

> 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)



Online SLAM

-----------



For environments without considerable loops this solution can be accurate and very efficient. It can run in *real time* even on a low-spec computer (we have it running on a turtlebot with a raspberry pi 3B+). It uses our localization algorithm combined with a dynamic likelihood field to incrementally build an occupancy map.



For more information please read

> Eurico Pedrosa, Artur Pereira, Nuno Lau\

> **A Non-Linear Least Squares Approach to SLAM using a Dynamic Likelihood Field**\

> Journal of Intelligent & Robotic Systems 93 (3-4), 519-532



Multi-threaded Particle Filter SLAM

--------------------



This Particle Filter SLAM is a RBPF SLAM like [GMapping](https://openslam-org.github.io/) and it is the extension of the Online SLAM solution to multiple particles with multi-thread support. Our solution is capable of parallelizing both the localization and mapping processes. It uses a thread-pool to manage the number of working threads.



Even without multi-threading, our solutions is a lightweight competitor against the heavyweight [GMapping](https://openslam-org.github.io/).



For more information please read

>Eurico Pedrosa, Artur Pereira, Nuno Lau\

> **Fast Grid SLAM Based on Particle Filter with Scan Matching and Multithreading**\

> 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Ponta Delgada, Portugal, 2020, pp. 194-199, doi: 10.1109/ICARSC49921.2020.9096191.



Graph SLAM

----------



A really **fast** graph based solution.