Keyframe-based visual–inertial odometry using nonlinear optimization

Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate visual–inertial odometry or simultaneous localization and mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that nonlinear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a rigorously probabilistic cost function that combines reprojection errors of landmarks and inertial terms. The problem is kept tractable and thus ensuring real-time operation by limiting the optimization to a bounded window of keyframes through marginalization. Keyframes may be spaced in time by arbitrary intervals, while still related by linearized inertial terms. We present evaluation results on complementary datasets recorded with our custom-built stereo visual–inertial hardware that accurately synchronizes accelerometer and gyroscope measurements with imagery. A comparison of both a stereo and monocular version of our algorithm with and without online extrinsics estimation is shown with respect to ground truth. Furthermore, we compare the performance to an implementation of a state-of-the-art stochastic cloning sliding-window filter. This competitive reference implementation performs tightly coupled filtering-based visual–inertial odometry. While our approach declaredly demands more computation, we show its superior performance in terms of accuracy

Compact Q-Learning for Micro-robots

Scaling down robots to miniature size introduces many new challenges including memory and program size limitations, low processor performance and low power autonomy. In this paper we describe the concept and implementation of learning of safe-wandering and light following tasks on the autonomous micro-robots, Alice. We propose a simplified reinforcement learning algorithm based on one step Q-learning that is optimized in speed and memory consumption. This algorithm uses only integer-based sum operators and avoids floating-point and multiplication operators

Compact Q-Learning Optimized for Micro-robots with Processing and Memory Constraints

Scaling down robots to miniature size introduces many new challenges including memory and program size limitations, low processor performance and low power autonomy. In this paper we describe the concept and implementation of learning of a safewandering task with the autonomous micro-robots, Alice. We propose a simplified reinforcement learning algorithm based on one-step Qlearning that is optimized in speed and memory consumption. This algorithm uses only integer-based sum operators and avoids floatingpoint and multiplication operators. Finally, quality of learning is compared to a floating-point based algorithm

Direct Aggression and the Balance between Status and Affection Goals in Adolescence

Previous studies have shown that status goals motivate direct forms of interpersonal aggression. However, status goals have been studied mostly in isolation from affection goals. It is theorized that the means by which status and affection goals are satisfied change during adolescence, which can affect aggression. This is tested in a pooled sample of (pre)adolescents (N = 1536; 49% girls; ages 10-15), by examining associations between status goals and direct aggression and the moderating role of affection goals. As hypothesized, with increasing age, status goals were more strongly associated with direct aggression. Moreover, for older adolescents, status goals were only associated with aggression when affection goals were weak. These findings support the changing relationship between status goals and direct aggression during adolescence

Multiresolution mapping and informative path planning for UAV-based terrain monitoring

© 2017 IEEE. Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. However, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning in terrain monitoring using UAVs. Our strategy exploits the spatial correlation encoded in a Gaussian Process model as a prior for Bayesian data fusion with probabilistic sensors. This allows us to incorporate altitude-dependent sensor models for aerial imaging and perform constant-time measurement updates. The resulting maps are used to plan information-rich trajectories in continuous 3-D space through a combination of grid search and evolutionary optimization. We evaluate our framework on the application of agricultural biomass monitoring. Extensive simulations show that our planner performs better than existing methods, with mean error reductions of up to 45% compared to traditional 'lawnmower' coverage. We demonstrate proof of concept using a multirotor to map color in different environments

Octopus - An Autonomous Wheeled Climbing Robot

This paper presents an innovative off-road wheeled mobile robot, named Octopus, able to deal autonomously with obstacles in rough terrain without getting stuck. To achieve such a performance, the robot is equipped with tilt sensors and tactile wheels. The sophisticated locomotion mechanism of Octopus has 8 motorized wheels and a total of 15 degrees of freedom (14 of them are motorized). A two-dimensional static model and a controller are proposed. The inputs of the controller are the contact points with ground, the geometric angles of the articulations, and the direction of the gravity field. The outputs of the controller are the torques for the wheels, the torques for the forearms, and the position set point for the body

3D multi-robot patrolling with a two-level coordination strategy

Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks

SLAM with Corner Features Based on a Relative Map

This paper presents a solution to the Simultaneous Localization and Mapping (SLAM) problem in the stochastic map framework for a mobile robot navigating in an indoor environment. The approach is based on the concept of the relative map. The idea consists in introducing a map state, which only contains quantities invariant under translation and rotation. This is done in order to have a decoupling between the robot motion and the landmark estimation and therefore not to rely the landmark estimation on the unmodeled error sources of the robot motion. The case of the corner feature is here considered. The relative state estimated through the Kalman filter contains the distances and the relative orientations among the corners observed at the same time. Therefore, this state is invariant with respect to the robot configuration (translation and rotation). Finally, an environment containing structures consisting of several corners is also investigated. Real experiments carried out with a mobile robot equipped with a 360 deg laser range finder show the performance of the approach