The KITTI Vision Benchmark Suite

Method

Two-stage Local Binocular BA and GPU [st] [TLBBA]

Submitted on 13 Jun. 2014 13:10 by
Teng Cao (Zhejiang University)

Running time:		0.1 s
Environment:		1 Core @2.8GHz (C/C++)

Method Description:

In the first stage, use binocular BA to optimize the
motion and structure between consecutive frames;
In the second stage, use BA in the multi-view and
interleaved mode.

Parameters:

Latex Bibtex:

@inproceedings{Lu2013IV,
title={High-performance visual odometry with two-
stage local binocular BA and GPU},
author={Lu, Wei and Xiang, Zhiyu and Liu, Jilin},
booktitle={Intelligent Vehicles Symposium (IV),
2013 IEEE},
pages={1107--1112},
year={2013},
organization={IEEE}
}

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.