Attitude observability of a loosely-coupled GPS/Visual Odometry Integrated Navigation Filter

We present a novel method for integrating GPS position estimates with position and attitude estimates derived from visual odometry using a scheme similar to a classic loosely-coupled GPS/INS integration. Under such an arrangement, we derive the error dynamics of the system and develop a Kalman Filter for estimating the errors in position and attitude. Using a control-based approach to observability, we show that the errors in both position and attitude (including yaw) are fully observable when there is a component of acceleration perpendicular to the velocity vector in the navigation frame. Numerical simulations are performed to confirm the observability analysis.

Towards improvement of autonomous glider navigation accuracy through the use of regional ocean models

Autonomous underwater gliders are robust and widely-used ocean sampling platforms that are characterized by their endurance, and are one of the best approaches to gather subsurface data at the appropriate spatial resolution to advance our knowledge of the ocean environment. Gliders generally do not employ sophisticated sensors for underwater localization, but instead dead-reckon between set waypoints. Thus, these vehicles are subject to large positional errors between prescribed and actual surfacing locations. Here, we investigate the implementation of a large-scale, regional ocean model into the trajectory design for autonomous gliders to improve their navigational accuracy. We compute the dead-reckoning error for our Slocum gliders, and compare this to the average positional error recorded from multiple deployments conducted over the past year. We then compare trajectory plans computed on-board the vehicle during recent deployments to our prediction-based trajectory plans for 140 surfacing occurrences.

Networked robotic sensor platform deployments for use in coastal environmental assessment in southern California

Mobile sensor platforms such as Autonomous Underwater Vehicles (AUVs) and robotic surface vessels, combined with static moored sensors compose a diverse sensor network that is able to provide macroscopic environmental analysis tool for ocean researchers. Working as a cohesive networked unit, the static buoys are always online, and provide insight as to the time and locations where a federated, mobile robot team should be deployed to effectively perform large scale spatiotemporal sampling on demand. Such a system can provide pertinent in situ measurements to marine biologists whom can then advise policy makers on critical environmental issues. This poster presents recent field deployment activity of AUVs demonstrating the effectiveness of our embedded communication network infrastructure throughout southern California coastal waters. We also report on progress towards real-time, web-streaming data from the multiple sampling locations and mobile sensor platforms. Static monitoring sites included in this presentation detail the network nodes positioned at Redondo Beach and Marina Del Ray. One of the deployed mobile sensors highlighted here are autonomous Slocum gliders. These nodes operate in the open ocean for periods as long as one month. The gliders are connected to the network via a Freewave radio modem network composed of multiple coastal base-stations. This increases the efficiency of deployment missions by reducing operational expenses via reduced reliability on satellite phones for communication, as well as increasing the rate and amount of data that can be transferred. Another mobile sensor platform presented in this study are the autonomous robotic boats. These platforms are utilized for harbor and littoral zone studies, and are capable of performing multi-robot coordination while observing known communication constraints. All of these pieces fit together to present an overview of ongoing collaborative work to develop an autonomous, region-wide, coastal environmental observation and monitoring sensor network.

Error analysis and attitude observability of a monocular GPS/visual odometry integrated navigation filter

In this paper, we present a method for the recovery of position and absolute attitude (including pitch, roll and yaw) using a novel fusion of monocular Visual Odometry and GPS measurements in a similar manner to a classic loosely-coupled GPS/INS error state navigation filter. The proposed filter does not require additional restrictions or assumptions such as platform-specific dynamics, map-matching, feature-tracking, visual loop-closing, gravity vector or additional sensors such as an IMU or magnetic compass. An observability analysis of the proposed filter is performed, showing that the scale factor, position and attitude errors are fully observable under acceleration that is non-parallel to velocity vector in the navigation frame. The observability properties of the proposed filter are demonstrated using numerical simulations. We conclude the article with an implementation of the proposed filter using real flight data collected from a Cessna 172 equipped with a downwards-looking camera and GPS, showing the feasibility of the algorithm in real-world conditions.

Which way from here? The spatiality of maps & navigation in first-person single-player video games

Player experience of spatiality in first-person, single-player games is informed by the maps and navigational aids provided by the game. This project uses textual analysis to examine the way these maps and navigational aids inform the experience of spatiality in Fallout 3, BioShock and BioShock 2. Spatiality is understood as trialectic, incorporating perceived, conceived and lived space, drawing on the work of Henri Lefebvre and Edward Soja. The most prominent elements of the games’ maps and navigational aids are analysed in terms of how they inform players’ experience of the games’ spaces. In particular this project examines the in-game maps these games incorporate, the waypoint navigation and fast-travel systems in Fallout 3, and the guide arrow and environmental cues in the BioShock games.

Multi-sensor data fusion for UAV navigation during landing operations

This paper presents a practical framework to synthesize multi-sensor navigation information for localization of a rotary-wing unmanned aerial vehicle (RUAV) and estimation of unknown ship positions when the RUAV approaches the landing deck. The estimation performance of the visual tracking sensor can also be improved through integrated navigation. Three different sensors (inertial navigation, Global Positioning System, and visual tracking sensor) are utilized complementarily to perform the navigation tasks for the purpose of an automatic landing. An extended Kalman filter (EKF) is developed to fuse data from various navigation sensors to provide the reliable navigation information. The performance of the fusion algorithm has been evaluated using real ship motion data. Simulation results suggest that the proposed method can be used to construct a practical navigation system for a UAV-ship landing system.

Towards lifelong navigation and mapping in an office environment

This paper addresses the challenge of developing robots that map and navigate autonomously in real world, dynamic environments throughout the robot’s entire lifetime – the problem of lifelong navigation. Static mapping algorithms can produce highly accurate maps, but have found few applications in real environments that are in constant flux. Environments change in many ways: both rapidly and gradually, transiently and permanently, geometrically and in appearance. This paper demonstrates a biologically inspired navigation algorithm, RatSLAM, that uses principles found in rodent neural circuits. The algorithm is demonstrated in an office delivery challenge where the robot was required to perform mock deliveries to goal locations in two different buildings. The robot successfully completed 1177 out of 1178 navigation trials over 37 hours of around the clock operation spread over 11 days.

Autonomous aerial navigation and tracking of marine animals

In this paper, we describe the development of an independent and on-board visual servoing system which allows a computationally impoverished aerial vehicle to autonomously identify and track a moving surface target. Our image segmentation and target identification algorithms were developed with the specific task of monitoring whales at sea but could be adapted for other targets. Observing whales is important for many marine biology tasks and is currently performed manually from the shore or from boats. We also present hardware experiments which demonstrate the capabilities of our algorithms for object identification and tracking that enable a flying vehicle to track a moving target.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

Cross-entropy optimization for scaling factors of a fuzzy controller : a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

Skype : a communications framework for robotics

This paper describes an architecture for robotic telepresence and teleoperation based on the well known tools ROS and Skype. We discuss how Skype can be used as a framework for robotic communication and can be integrated into a ROS/Linux framework to allow a remote user to not only interact with people near the robot, but to view maps, sensory data, robot pose and to issue commands to the robot’s navigation stack. This allows the remote user to exploit the robot’s autonomy, providing a much more convenient navigation interface than simple remote joysticking.

One-stop government portals : transformation or navigation?

E-government is seen as a promising approach for governments to improve their service towards citizens and become more cost-efficient in service delivery. This is often combined with one-stop government, which is a citizen-oriented approach stressing integrated provision of services from multiple departments via a single access point, the one-stop government portal. While the portal concept is gaining prominence in practice, there is little know about its status in academic literature. This hinders academics in building an accumulated body of knowledge around the concept and makes it hard for practitioners to access relevant academic insights on the topic. The objective of this study is to identify and understand the key themes of the one-stop government portal concept in academic, e-government research. A holistic analysis is provided by addressing different viewpoints: social-political, legal, organizational, user, security, service, data & information, and technical. As overall finding we conclude that there are two different approaches: a more pragmatic approach focuses on quick wins in particular related to usability and navigation and a more ambitious, transformational approach having far reaching social-political, legal, organizational implications.

Nonparametric semantic segmentation for 3D street scenes

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.

Vision-only autonomous navigation using topometric maps

This paper presents a mapping and navigation system for a mobile robot, which uses vision as its sole sensor modality. The system enables the robot to navigate autonomously, plan paths and avoid obstacles using a vision based topometric map of its environment. The map consists of a globally-consistent pose-graph with a local 3D point cloud attached to each of its nodes. These point clouds are used for direction independent loop closure and to dynamically generate 2D metric maps for locally optimal path planning. Using this locally semi-continuous metric space, the robot performs shortest path planning instead of following the nodes of the graph --- as is done with most other vision-only navigation approaches. The system exploits the local accuracy of visual odometry in creating local metric maps, and uses pose graph SLAM, visual appearance-based place recognition and point clouds registration to create the topometric map. The ability of the framework to sustain vision-only navigation is validated experimentally, and the system is provided as open-source software.

Monocular vision based autonomous navigation for a cost-effective MAV in GPS-denied environments

In this paper, we present a monocular vision based autonomous navigation system for Micro Aerial Vehicles (MAVs) in GPS-denied environments. The major drawback of monocular systems is that the depth scale of the scene can not be determined without prior knowledge or other sensors. To address this problem, we minimize a cost function consisting of a drift-free altitude measurement and up-to-scale position estimate obtained using the visual sensor. We evaluate the scale estimator, state estimator and controller performance by comparing with ground truth data acquired using a motion capture system. All resources including source code, tutorial documentation and system models are available online.