Physics

Trajectory

Business

Mobile robot

Gene

Astronomy

Epistemology

Marketing

Philosophy

Inertial frame of reference

Artificial intelligence

Chemistry

Transformation (genetics)

Modality (human–computer interaction)

Computer science

Odometry

Visual odometry

Quantum mechanics

Strengths and weaknesses

Computer vision

Biochemistry

Monocular

Benchmarking

Robot

In this tutorial, we provide principled methods to quantitatively evaluate the quality of an estimated trajectory from visual(-inertial) odometry (VO/VIO), which is the foundation of benchmarking the accuracy of different algorithms. First, we show how to determine the transformation type to use in trajectory alignment based on the specific sensing modality (i.e., monocular, stereo and visual-inertial). Second, we describe commonly used error metrics (i.e., the absolute trajectory error and the relative error) and their strengths and weaknesses. To make the methodology presented for VO/VIO applicable to other setups, we also generalize our formulation to any given sensing modality. To facilitate the reproducibility of related research, we publicly release our implementation of the methods described in this tutorial.

A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry