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PyICP SLAM

Full-python LiDAR SLAM.

(if you find a C++ version of this repo, go to https://github.com/irapkaist/SC-LeGO-LOAM)

Purpose

  • Full-python LiDAR SLAM
    • Easy to exchange or connect with any Python-based components (e.g., DL front-ends such as Deep Odometry)
      • Here, ICP, which is a very basic option for LiDAR, and Scan Context (IROS 18) are used for odometry and loop detection, respectively.
  • Hands-on LiDAR SLAM
    • Easy to understand (could be used for educational purpose)
  • The practical use case of miniSAM
    • The miniSAM is easy to use at Python

What is SLAM?

  • In this repository, SLAM (Simultaneous localization and mapping) is considered as
    • SLAM = Odometry + Loop closure
  • In this repository, the state to be optimized only has robot poses; that is pose-graph SLAM.

Overview of the PyICP SLAM

  • The pipeline of the PyICP SLAM is composed of three parts
    1. Odometry: ICP (iterative closest point)
      • In here, Point-to-point and frame-to-frame (i.e., no local mapping)
    2. Loop detection: Scan Context (IROS 18)
      • Reverse loop detection is supported.
    3. Back-end (graph optimizer): miniSAM
      • Python API

Features

  • Thanks to the Scan Context, reverse loops can be successfully closed.

    • E.g., see KITTI 14 at Results section below.
  • Time costs

    • (Here, no accelerated and naive) ICP gets 7-10 Hz for randomly downsampled points (7000 points)
    • (Here, no accelerated and naive) Scan Context gets 1-2 Hz (when 10 ringkey candidates).
    • miniSAM is enough fast.

How to use

Just run

$ python3 main_icp_slam.py

The details of parameters are eaily found in the argparser in that .py file.

Results (KITTI dataset)

Those results are produced under the same parameter conditions:

  • ICP used random downsampling, 7000 points.
  • Scan Context's parameters:
    • Ring: 20, Sector: 60
    • The number of ringkey candidates: 30
    • Correct Loop threshold: 0.17 for 09, 0.15 for 14, and 0.11 for all others

Results (left to right):

  • 00 (loop), 01, 02 (loop), 03

  • 04, 05 (loop), 06 (loop), 09 (loop)

  • 10, 11, 12, 13 (loop)

  • 14 (loop), 15 (loop), 16 (loop), 17

  • 18 (loop), 20

Some of the results are good, and some of them are not enough. Those results are for the study to understand when is the algorithm works or not.

Findings

Author

  Giseop Kim ([email protected])

Contirbutors

  @JustWon
    - Supports Pangolin-based point cloud visualization along the SLAM poses.
    - Go to https://github.com/JustWon/PyICP-SLAM

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python (50,967
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scan (38
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