Direct Sparse Odometry

TL;DR

This paper introduces a real-time direct sparse visual odometry method that minimizes photometric error without relying on keypoints, achieving high accuracy and robustness across diverse environments.

Contribution

It presents a novel direct probabilistic model that jointly optimizes geometry and motion, sampling pixels evenly and incorporating full photometric calibration.

Findings

Outperforms state-of-the-art methods in accuracy

Demonstrates robustness across various datasets

Operates in real time without keypoint detection

Abstract

We propose a novel direct sparse visual odometry formulation. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry -- represented as inverse depth in a reference frame -- and camera motion. This is achieved in real time by omitting the smoothness prior used in other direct methods and instead sampling pixels evenly throughout the images. Since our method does not depend on keypoint detectors or descriptors, it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on mostly white walls. The proposed model integrates a full photometric calibration, accounting for exposure time, lens vignetting, and non-linear response functions. We thoroughly evaluate our method on three different datasets comprising several hours of video. The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness.