A unified vision and inertial navigation system for planetary hoppers

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (pages 139-146).In recent years, considerable attention has been paid to hopping as a novel mode of planetary exploration. Hopping vehicles provide advantages over traditional surface exploration vehicles, such as wheeled rovers, by enabling in-situ measurements in otherwise inaccessible terrain. However, significant development over previously demonstrated vehicle navigation technologies is required to overcome the inherent challenges involved in navigating a hopping vehicle, especially in adverse terrain. While hoppers are in many ways similar to traditional landers and surface explorers, they incorporate additional, unique motions that must be accounted for beyond those of conventional planetary landing and surface navigation systems. This thesis describes a unified vision and inertial navigation system for propulsive planetary hoppers and provides demonstration of this technology. An architecture for a navigation system specific to the motions and mission profiles of hoppers is presented, incorporating unified inertial and terrain-relative navigation solutions. A modular sensor testbed, including a stereo vision package and inertial measurement unit, was developed to act as a proof-of-concept for this navigation system architecture. The system is shown to be capable of real-time output of an accurate navigation state estimate for motions and trajectories similar to those of planetary hoppers.by Theodore J. Steiner, III.S.M

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Egospace Motion Planning Representations for Micro Air Vehicles

Navigation of micro air vehicles (MAVs) in unknown environments is a complex sensing and trajectory generation task, particularly at high velocities. In this work, we introduce an efficient sense-and-avoid pipeline that compactly represents range measurements from multiple sensors, trajectory generation, and motion planning in a 2.5–dimensional projective data structure called an egospace representation. Egospace coordinates generalize depth image obstacle representations and are a particularly convenient choice for configuration flat mobile robots, which are differentially flat in their configuration variables and include a number of commonly used MAV plant models. After characterizing egospace obstacle avoidance for robots with trivial dynamics and establishing limits on applicability and performance, we generalize to motion planning over full configuration flat dynamics using motion primitives expressed directly in egospace coordinates. In comparison to approaches based on world coordinates, egospace uses the natural sensor geometry to combine the benefits of a multi-resolution and multi-sensor representation architecture into a single simple and efficient layer. We also present an experimental implementation, based on perception with stereo vision and an egocylinder obstacle representation, that demonstrates the specialization of our theoretical results to particular mission scenarios. The natural pixel parameterization of the egocylinder is used to quickly identify dynamically feasible maneuvers onto radial paths, expressed directly in egocylinder coordinates, that enable finely detailed planning at extreme ranges within milliseconds. We have implemented our obstacle avoidance pipeline with an Asctec Pelican quadcopter, and demonstrate the efficiency of our approach experimentally with a set of challenging field scenarios. The scalability potential of our system is discussed in terms of sensor horizon, actuation, and computational limitations and the speed limits that each imposes, and its generality to more challenging environments with multiple moving obstacles is developed as an immediate extension to the static framework

Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Monocular Vision based Particle Filter Localization in Urban Environments

This thesis presents the design and experimental result of a monocular vision based particle filter localization system for urban settings that uses aerial orthoimagery as a reference map. The topics of perception and localization are reviewed along with their modeling using a probabilistic framework. Computer vision techniques used to create the feature map and to extract features from camera images are discussed. Localization results indicate that the design is viable

User-oriented markerless augmented reality framework based on 3D reconstruction and loop closure detection

An augmented reality (AR) system needs to track the user-view to perform an accurate augmentation registration. The present research proposes a conceptual marker-less, natural feature-based AR framework system, the process for which is divided into two stages - an offline database training session for the application developers, and an online AR tracking and display session for the final users. In the offline session, two types of 3D reconstruction application, RGBD-SLAM and SfM are integrated into the development framework for building the reference template of a target environment. The performance and applicable conditions of these two methods are presented in the present thesis, and the application developers can choose which method to apply for their developmental demands. A general developmental user interface is provided to the developer for interaction, including a simple GUI tool for augmentation configuration. The present proposal also applies a Bag of Words strategy to enable a rapid "loop-closure detection" in the online session, for efficiently querying the application user-view from the trained database to locate the user pose. The rendering and display process of augmentation is currently implemented within an OpenGL window, which is one result of the research that is worthy of future detailed investigation and development

Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications