Information-Driven Direct RGB-D Odometry

This paper presents an information-theoretic approach to point selection for direct RGB-D odometry. The aim is to select only the most informative measurements, in order to reduce the optimization problem with a minimal impact in the accuracy. It is usual practice in visual odometry/SLAM to track several hundreds of points, achieving real-time performance in high-end desktop PCs. Reducing their computational footprint will facilitate the implementation of odometry and SLAM in low-end platforms such as small robots and AR/VR glasses. Our experimental results show that our novel information-based selection criteria allows us to reduce the number of tracked points an order of magnitude (down to only 24 of them), achieving an accuracy similar to the state of the art (sometimes outperforming it) while reducing 10× the computational demand

A Model for Multi-View Residual Covariances based on Perspective Deformation

In this work, we derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. The core of our approach is the formulation of the residual covariances as a combination of geometric and photometric noise sources. And our key novel contribution is the derivation of a term modelling how local 2D patches suffer from perspective deformation when imaging 3D surfaces around a point. Together, these add up to an efficient and general formulation which not only improves the accuracy of both feature-based and direct methods, but can also be used to estimate more accurate measures of the state entropy and hence better founded point visibility thresholds. We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment, improving their accuracy with a negligible overhead

Preliminary Results for the Multi-Robot, Multi-Partner, Multi-Mission, Planetary Exploration Analogue Campaign on Mount Etna

This paper was initially intended to report on the outcome of the twice postponed demonstration mission of the ARCHES project. Due to the global COVID pandemic, it has been postponed from 2020, then 2021, to 2022. Nevertheless, the development of our concepts and integration has progressed rapidly, and some of the preliminary results are worthwhile to share with the community to drive the dialog on robotics planetary exploration strategies. This paper includes an overview of the planned 4-week campaign, as well as the vision and relevance of the missiontowards the planned official space missions. Furthermore, the cooperative aspect of the robotic teams, the scientific motivation, the sub task achievements are summarised

The MADMAX data set for visual‐inertial rover navigation on Mars

Planetary rovers increasingly rely on vision-based components for autonomous navigation and mapping. Developing and testing these components requires representative optical conditions, which can be achieved by either field testing at planetary analog sites on Earth or using prerecorded data sets from such locations. However, the availability of representative data is scarce and field testing in planetary analog sites requires a substantial financial investment and logistical overhead, and it entails the risk of damaging complex robotic systems. To address these issues, we use our compact human-portable DLR Sensor Unit for Planetary Exploration Rovers (SUPER) in the Moroccan desert to show resource-efficient field testing and make the resulting Morocco-Acquired data set of Mars-Analog eXploration (MADMAX) publicly accessible. The data set consists of 36 different navigation experiments, captured at eight Mars analog sites of widely varying environmental conditions. Its longest trajectory covers 1.5 km and the combined trajectory length is 9.2 km. The data set contains time-stamped recordings from monochrome stereo cameras, a color camera, omnidirectional cameras in stereo configuration, and from an inertial measurement unit. Additionally, we provide the ground truth in position and orientation together with the associated uncertainties, obtained by a real-time kinematic-based algorithm that fuses the global navigation satellite system data of two body antennas. Finally, we run two state-of-the-art navigation algorithms, ORB-SLAM2 and VINS-mono, on our data to evaluate their accuracy and to provide a baseline, which can be used as a performance reference of accuracy and robustness for other navigation algorithms. The data set can be accessed at https://rmc.dlr.de/morocco2018