5 research outputs found
Adaptive Localization and Mapping for Planetary Rovers
Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach
Event-aided Direct Sparse Odometry
We introduce EDS, a direct monocular visual odometry using events and frames.
Our algorithm leverages the event generation model to track the camera motion
in the blind time between frames. The method formulates a direct probabilistic
approach of observed brightness increments. Per-pixel brightness increments are
predicted using a sparse number of selected 3D points and are compared to the
events via the brightness increment error to estimate camera motion. The method
recovers a semi-dense 3D map using photometric bundle adjustment. EDS is the
first method to perform 6-DOF VO using events and frames with a direct
approach. By design, it overcomes the problem of changing appearance in
indirect methods. We also show that, for a target error performance, EDS can
work at lower frame rates than state-of-the-art frame-based VO solutions. This
opens the door to low-power motion-tracking applications where frames are
sparingly triggered "on demand" and our method tracks the motion in between. We
release code and datasets to the public.Comment: 16 pages, 14 Figures, Page: https://rpg.ifi.uzh.ch/ed
Concept, Development and Testing of Mars Rover Prototypes for ESA Planetary Exploration
This paper presents the system architecture and design of two planetary rover laboratory prototypes developed at the European Space Agency (ESA). These research platforms have been developed to provide early prototypes for validation of designs and serve ESA鈥檚 Automation & Robotics Lab infrastructure as testbeds for continuous research and testing. Both rovers have been built considering the constraints of Space Systems with the sufficient level of representativeness to allow rapid prototyping. They avoid strictly space-qualified components and designs that present a major cost burden and frequently lack the flexibility or modularity that the lab environment requires for its investigations. This design approach is followed for all the mechanical, electrical, and software aspects of the system. In this paper, two ExoMars mission-representative rovers, the ExoMars Testing Rover (ExoTeR) and the Martian Rover Testbed for Autonomy (MaRTA), are thoroughly described. The lessons learnt and experience gained while running several research activities and test campaigns are also presented. Finally, the paper aims to
provide some insight on how to reduce the gap between lab R&D and flight implementation by anticipating system constraints
when building and testing these platforms
Adaptive Lokalisierung und Kartographierung f眉r Weltraum-Rover
Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach
Autonomous Power Line Inspection with Drones via Perception-Aware MPC
Drones have the potential to revolutionize power line inspection by
increasing productivity, reducing inspection time, improving data quality, and
eliminating the risks for human operators. Current state-of-the-art systems for
power line inspection have two shortcomings: (i) control is decoupled from
perception and needs accurate information about the location of the power lines
and masts; (ii) collision avoidance is decoupled from the power line tracking,
which results in poor tracking in the vicinity of the power masts, and,
consequently, in decreased data quality for visual inspection. In this work, we
propose a model predictive controller (MPC) that overcomes these limitations by
tightly coupling perception and action. Our controller generates commands that
maximize the visibility of the power lines while, at the same time, safely
avoiding the power masts. For power line detection, we propose a lightweight
learning-based detector that is trained only on synthetic data and is able to
transfer zero-shot to real-world power line images. We validate our system in
simulation and real-world experiments on a mock-up power line infrastructure