The KITTI Vision Benchmark Suite

Method

Stereo-RIVO: Stereo-Robust Indirect Visual Odometry [Stereo-RIVO]

Submitted on 8 Jun. 2023 14:51 by
erfan salehi (student)

Running time:		0.07 s
Environment:		4 cores @ 2.5 Ghz (Matlab)

Method Description:

Many VO systems prioritize achieving accuracy and speed, they often come with a high degree of complexity and limited robustness. To overcome these challenges, this paper aims to propose a new VO system (Stereo-RIVO) that balances accuracy, speed, and computational cost. Furthermore, this algorithm is based on a new data association module which consists of two primary components: a scene-matching process that achieves exceptional precision without feature extraction and a key-frame detection technique based on a stochastic model of scene movement.

Parameters:

alpha=0.2

Latex Bibtex:

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@article{SLAEHI,
title = {Stereo-RIVO: Stereo-Robust Indirect Visual Odometry},
journal = {Expert Systems with Applications},
year = {2023},
author = {Erfan Salehi, Ali Aghagolzadeh, Reshad Hosseini},
keywords = {visual odometry, scene-matching, key-frame detection, epipolar geometry, phase analyzing, inlier-detection}}

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.