Experiments in outdoor operation of RatSLAM

This paper shows initial results in deploying the biologically inspired Simultaneous Localisation and Mapping system, RatSLAM, in an outdoor environment. RatSLAM has been widely tested in indoor environments on the task of producing topologically coherent maps based on a fusion of odometric and visual information. This paper details the changes required to deploy RatSLAM on a small tractor equipped with odometry and an omnidirectional camera. The principal changes relate to the vision system, with others required for RatSLAM to use omnidirectional visual data. The initial results from mapping around a 500 m loop are promising, with many improvements still to be made.

Simultaneous localisation and mapping from natural landmarks using RatSLAM

This paper describes the current state of RatSLAM, a Simultaneous Localisation and Mapping (SLAM) system based on models of the rodent hippocampus. RatSLAM uses a competitive attractor network to fuse visual and odometry information. Energy packets in the network represent pose hypotheses, which are updated by odometry and can be enhanced or inhibited by visual input. This paper shows the effectiveness of the system in real robot tests in unmodified indoor environments using a learning vision system. Results are shown for two test environments; a large corridor loop and the complete floor of an office building.

Framework for the long-term operation of a mobile robot via the Internet

This paper describes an autonomous docking system and web interface that allows long-term unaided use of a sophisticated robot by untrained web users. These systems have been applied to the biologically inspired RatSLAM system as a foundation for testing both its long-term stability and its practicality. While docking and web interface systems already exist, this system allows for a significantly larger margin of error in docking accuracy due to the mechanical design, thereby increasing robustness against navigational errors. Also a standard vision sensor is used for both long-range and short-range docking, compared to the many systems that require both omni-directional cameras and high resolution Laser range finders for navigation. The web interface has been designed to accommodate the significant delays experienced on the Internet, and to facilitate the non- Cartesian operation of the RatSLAM system.

Obstacle detection using optical flow

Motion has been examined in biology to be a critical component for obstacle avoidance and navigation. In particular, optical flow is a powerful motion cue that has been exploited in many biological systems for survival. In this paper, we investigate an obstacle detection system that uses optical flow to obtain range information to objects. Our experimental results demonstrate that optical flow is capable of providing good obstacle information but has obvious failure modes. We acknowledge that our optical flow system has certain disadvantages and cannot be solely used for navigation. Instead, we believe that optical flow is a critical visual subsystem used when moving at reason- able speeds. When combined with other visual subsystems, considerable synergy can result.

Fuzzy associative memory for humanoid robot joint control

Traditional approaches to joint control required accurate modelling of the system dynamic of the plant in question. Fuzzy Associative Memory (FAM) control schemes allow adequate control without a model of the system to be controlled. This paper presents a FAM based joint controller implemented on a humanoid robot. An empirically tuned PI velocity control loop is augmented with this feed forward FAM, with considerable reduction in joint position error achieved online and with minimal additional computational overhead.

Experience mapping : producing spatially continuous environment representations using RatSLAM

RatSLAM is a system for vision-based Simultaneous Localisation and Mapping (SLAM) inspired by models of the rodent hippocampus. The system can produce stable representations of large complex environments during robot experiments in both indoor and outdoor environments. These representations are both topological and metric in nature, and can involve multiple representations of the same place as well as discontinuities. In this paper we describe a new technique known as experience mapping that can be used online with the RatSLAM system to produce world representations known as experience maps. These maps group together multiple place representations and are spatially continuous. A number of experiments have been conducted in simulation and a real world office environment. These experiments demonstrate the high degree to which experience maps are representative of the spatial arrangement of the environment.

Featureless vehicle-based visual SLAM with a consumer camera

The Simultaneous Localisation And Mapping (SLAM) problem is one of the major challenges in mobile robotics. Probabilistic techniques using high-end range finding devices are well established in the field, but recent work has investigated vision-only approaches. We present an alternative approach to the leading existing techniques, which extracts approximate rotational and translation velocity information from a vehicle-mounted consumer camera, without tracking landmarks. When coupled with an existing SLAM system, the vision module is able to map a 45 metre long indoor loop and a 1.6 km long outdoor road loop, without any parameter or system adjustment between tests. The work serves as a promising pilot study into ground-based vision-only SLAM, with minimal geometric interpretation of the environment.

Learning to avoid indoor obstacles from optical flow

Optical flow (OF) is a powerful motion cue that captures the fusion of two important properties for the task of obstacle avoidance − 3D self-motion and 3D environmental surroundings. The problem of extracting such information for obstacle avoidance is commonly addressed through quantitative techniques such as time-to-contact and divergence, which are highly sensitive to noise in the OF image. This paper presents a new strategy towards obstacle avoidance in an indoor setting, using the combination of quantitative and structural properties of the OF field, coupled with the flexibility and efficiency of a machine learning system.The resulting system is able to effectively control the robot in real-time, avoiding obstacles in familiar and unfamiliar indoor environments, under given motion constraints. Furthermore, through the examination of the networks internal weights, we show how OF properties are being used toward the detection of these indoor obstacles.

Assessing the safety of a velocity sourced series elastic actuator using the head injury criterion

The Velocity Sourced Series Elastic Actuator has been proposed as a method for providing safe force or torque based actuation for robots without compromising the actuator performance. In this paper we assess the safety of Velocity Sourced Series Elastic Actuators by measuring the Head Injury Criterion scores for collisions with a model head. The study makes a comparative analysis against stiff, high impedance actuation using the same motor without the series elastic component, showing that the series elastic component brings about a massive reduction in the chance of head injury. The benefits of a collision detection and safe reaction system are shown to be limited to collisions at low speeds, providing greater interaction comfort but not necessarily contributing to safety from injury.

Reading the play : adaptation by prediction of agent motion

An adaptive agent improves its performance by learning from experience. This paper describes an approach to adaptation based on modelling dynamic elements of the environment in order to make predictions of likely future state. This approach is akin to an elite sports player being able to “read the play”, allowing for decisions to be made based on predictions of likely future outcomes. Modelling of the agent‟s likely future state is performed using Markov Chains and a technique called “Motion and Occupancy Grids”. The experiments in this paper compare the performance of the planning system with and without the use of this predictive model. The results of the study demonstrate a surprising decrease in performance when using the predictions of agent occupancy. The results are derived from statistical analysis of the agent‟s performance in a high fidelity simulation of a world leading real robot soccer team.

Augmenting RatSLAM using FAB-MAP-based visual data association

This paper investigates the use of the FAB-MAP appearance-only SLAM algorithm as a method for performing visual data association for RatSLAM, a semi-metric full SLAM system. While both systems have shown the ability to map large (60-70km) outdoor locations of approximately the same scale, for either larger areas or across longer time periods both algorithms encounter difficulties with false positive matches. By combining these algorithms using a mapping between appearance and pose space, both false positives and false negatives generated by FAB-MAP are significantly reduced during outdoor mapping using a forward-facing camera. The hybrid FAB-MAP-RatSLAM system developed demonstrates the potential for successful SLAM over large periods of time.

Automatic calibration of a spiking head-direction network for representing robot orientation

Calibration of movement tracking systems is a difficult problem faced by both animals and robots. The ability to continuously calibrate changing systems is essential for animals as they grow or are injured, and highly desirable for robot control or mapping systems due to the possibility of component wear, modification, damage and their deployment on varied robotic platforms. In this paper we use inspiration from the animal head direction tracking system to implement a self-calibrating, neurally-based robot orientation tracking system. Using real robot data we demonstrate how the system can remove tracking drift and learn to consistently track rotation over a large range of velocities. The neural tracking system provides the first steps towards a fully neural SLAM system with improved practical applicability through selftuning and adaptation.

Demonstrating the safety and performance of a velocity sourced series elastic actuator

Actuators with deliberately added compliant elements in the transmission system are often described as improving the safety of the actuator at the detriment of the performance. We show that our variant of the Series Elastic Actuator topology, the Velocity Sourced Series Elastic Actuator, has well defined performance characteristics that make for improvements in safety and performance over conventional high impedance actuators. The improvement in performance was principally achieved by having tight velocity control of the DC motor that acts as the mechanical power source for the actuator. Results for performance are given for point to point transition times, while results for safety are based on empirical assessment of the Head Injury Criterion during collisions.

Single camera vision-only SLAM on a suburban road network

Simultaneous Localization And Mapping (SLAM) is one of the major challenges in mobile robotics. Probabilistic techniques using high-end range finding devices are well established in the field, but recent work has investigated vision only approaches. This paper presents a method for generating approximate rotational and translation velocity information from a single vehicle-mounted consumer camera, without the computationally expensive process of tracking landmarks. The method is tested by employing it to provide the odometric and visual information for the RatSLAM system while mapping a complex suburban road network. RatSLAM generates a coherent map of the environment during an 18 km long trip through suburban traffic at speeds of up to 60 km/hr. This result demonstrates the potential of ground based vision-only SLAM using low cost sensing and computational hardware.

RatSLAM on the edge : revealing a coherent representation from an overloaded rat brain

The RatSLAM system can perform vision based SLAM using a computational model of the rodent hippocampus. When the number of pose cells used to represent space in RatSLAM is reduced, artifacts are introduced that hinder its use for goal directed navigation. This paper describes a new component for the RatSLAM system called an experience map, which provides a coherent representation for goal directed navigation. Results are presented for two sets of real world experiments, including comparison with the original goal memory system's performance in the same environment. Preliminary results are also presented demonstrating the ability of the experience map to adapt to simple short term changes in the environment.