The KITTI Vision Benchmark Suite

Method

Tight-coupled visual lidar SLAM and calibration with outlier rejection[st][la][lc] [TVL-SLAM+]

Submitted on 4 Feb. 2021 09:13 by
Chih Chung Chou (National Taiwan University)

Running time:		0.3 s
Environment:		1 core @ 3.0 Ghz (C/C++)

Method Description:

Visual-lidar SLAM algorithm, the front-ends are
running independently, but the back-ends(local and
global bundle adjustments) are solved in a tightly-
coupled way. Both visual and lidar loop detections
are incorporated.

Filtering the moving visual feature points with the
assistance of lidar odometry. Camera-lidar
extrinsics are calibrated using this SLAM pipeline,
by optimizing the extrinsics along with keyframes
and map points.

Parameters:

lidar grid size is multi-resolution, 3m/1m, with
different weights.

Latex Bibtex:

@ARTICLE{9632274,
author={Chou, Chih-Chung and Chou, Cheng-Fu},
journal={IEEE Transactions on Intelligent
Transportation Systems},
title={Efficient and Accurate Tightly-Coupled
Visual-Lidar SLAM},
year={2021},
volume={},
number={},
pages={1-15},
doi={10.1109/TITS.2021.3130089}}

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.