PyTorch LO-Net

Deep Real-time Lidar Odometry

This is an unofficial notebook implementation of the LO-Net paper (https://arxiv.org/abs/1904.08242), a deep convolutional network pipeline for real-time lidar odometry estimation.

Update:

The project was started during an internship and is still unfinished with regards to training the network on sufficient data. Results are still very much sub-optimal :(

Network overview

Comparison of methods for Normal estimation

Paper	Open3d

Dependencies

• pytorch (> v1.7)
• tensorboard
• scipy
• open3d
• tqdm (optional)

Dataset (KITTI)

Instructions for downloading the dataset can be found at http://www.cvlibs.net/datasets/kitti/eval_odometry.php

At the end, sequences should be extracted under a directory as follows:

KITTI
|
 sequences|
        |-> 00
            |-> image0 ...
            |-> velodyne
            |-> calib.txt
            |-> times.txt
        .
        .
        .
        |-> 21
            .
            .
            .
 poses|
        |-> 00.txt
        .
        .
        |-> 10.txt