Pose SLAM is the variant of simultaneous localization and map building (SLAM) is the variant of SLAM, in which only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information filter form of Pose SLAM and, at the same time, to delay inconsistency as much as possible, we introduce an approach that takes into account only highly informative loop-closure links and nonredundant poses. This approach includes constant time procedures to compute the distance between poses, the expected information gain for each potential link, and the exact marginal covariances while moving in open loop, as well as a procedure to recover the state after a loop closure that, in practical situations, scales linearly in terms of both time and memory. Using these procedures, the robot operates most of the time in open loop, and the cost of the loop closure is amortized over long trajectories. This way, the computational bottleneck shifts to data association, which is the search over the set of previously visited poses to determine good candidates for sensor registration. To speed up data association, we introduce a method to search for neighboring poses whose complexity ranges from logarithmic in the usual case to linear in degenerate situations. The method is based on organizing the pose information in a balanced tree whose internal levels are defined using interval arithmetic. The proposed Pose-SLAM approach is validated through simulations, real mapping sessions, and experiments using standard SLAM data sets.

We propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate than iterative ones. Our approach is inspired on the EPnP algorithm, a recent O(n) solution for the calibrated case. Yet we show that considering the focal length as an additional unknown renders the linearization and relinearization techniques of the original approach no longer valid, especially with large amounts of noise. We present new methodologies to circumvent this limitation termed exhaustive linearization and exhaustive relinearization which perform a systematic exploration of the solution space in closed form. The method is evaluated on both real and synthetic data, and our results show that besides producing precise focal length estimation, the retrieved camera pose is almost as accurate as the one computed using the EPnP, which assumes a calibrated camera.

In this letter, a hybrid visual servoing with a hierarchical task-composition control framework is described for aerial manipulation, i.e., for the control of an aerial vehicle endowed with a robot arm. The proposed approach suitably combines into a unique hybrid-control framework the main benefits of both image-based and position-based control schemes. Moreover, the underactuation of the aerial vehicle has been explicitly taken into account in a general formulation, together with a dynamic smooth activation mechanism. Both simulation case studies and experiments are presented to demonstrate the performance of the proposed technique.

Automation of excavation operations can be realized by an automatically controlled excavator system that is able to perform autonomously a planned digging work and to quickly comply to interacting forces experienced during excavation. The development of such an automated control system is usually based on a dynamic model of the system that describes the motion with time. A dynamic model for an excavator that is needed for the controller design can be derived by applying Newton-Euler equations to each link in succession. The model obtained describes the motion of the excavator. It corrects several shortcomings that appear in previously published excavator model. On the basis of the model derived, a proportional-differential controller is designed that makes the bucket to track a specified trajectory. It can be used to automate the machine operations, for example, for terrestrial and planetary excavations as well as for mining applications.

Event-based cameras generate asynchronous streams of events, triggered proportionally to the logarithmic change of brightness in the scene. These cameras have very low latency and high dynamic range suitable to address challenging motion scenarios in robotics. In this work, we explore a new event-based line-SLAM approach following a parallel tracking and mapping philosophy. Our fast tracking algorithm, produces accurate camera pose estimates at a high rate by minimizing the event-line reprojection error with an error-state Kalman filter formulated entirely with Lie theory. The mapping thread leverages the natural edge highlighting strength of events to recover and optimize straight lines in human-made scenarios. The proper manipulation of matrix sparsity as well as the information sharing between tracking and mapping nodes allow us to achieve real-time performance on a standard multi-core CPU. This system was tested on several scenarios rich in straight edge objects, and compared against, ground truth and frame and event based state-of-the-art approaches.