The KITTI Vision Benchmark Suite

Method

Stereo-RIVO: Stereo-Robust Indirect Visual Odometry [stereo-RIVO]
[Anonymous Submission]

Submitted on 1 Jun. 2023 09:34 by
[Anonymous Submission]

Running time:		0.07 s
Environment:		8 cores @ 2.5 Ghz (Matlab)

Method Description:

While 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 (DA) module which consists of two primary components: a scene-matching (SM) process that achieves exceptional precision without feature extraction and a key-frame detection (KFD) technique based on a stochastic model of scene movement. The performance of this proposed VO system has been tested extensively in all sequences of KITTI datasets. The results of these tests indicate that the proposed Stereo-RIVO outperforms other state-of-the-art methods in terms of robustness, accuracy, and speed.

Parameters:

nothing

Latex Bibtex:

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.