Last updated on May 31, 2015. This conference program is tentative and subject to change
In this paper we develop the concept of self-tuning M-estimators. We first make the connection between many common M-estimators and elliptical probability distributions. This connection shows that the choice of M-estimator is an assumption that the residuals belong to a well-defined elliptical distribution. We exploit this implication in two ways. First, we develop an algorithm for tuning the M-estimator parameters during iterative optimization. Second, we show how to choose the correct M-estimator for your data by examining the likelihood of the data given the model. We fully derive these algorithms and show their behavior on a representative example of visual simultaneous localization and mapping.
To demonstrate the effectiveness and generalisation of the approach, we present evaluations using two datasets with different cameras. The first uses a hyperspectral sensor that allows us to analyse the results for a large number of wavelengths, while the second dataset uses a standard RGB camera. The approach is shown to consistently provide good compensation.
TeleoR has to enable the transparent programming of communicating robotic agents with an extended range of task behaviours. Our goal was to do this without losing the elegance and simplicity of Nilsson's language. We also wanted to be able to give TeleoR a formally defined operational semantics, building upon one we had given for TR. The extensions, their semantics, and their implementation were developed in parallel. A methodology we can recommend.
We first analyze the dynamics of the system to identify the limit cycles as well as the limitations of the control authority within the definition of walking. Then, we present the two-step deadbeat control policy that guarantees stability and with the fastest possible convergence for the system. For each equilibrium gait, the basin of attraction in which this two-step control exists is a measure of the robustness of the system. It is observed that human-like walking (double hump ground reaction force profile) has the largest basin of attraction. Finally, we extend the policy to various energy levels to accommodate walking on uneven ground that has height changes. We show in simulation that the system indeed rejects various disturbances and converges to the desired equilibrium gait in two steps.
It turns out that the CWC can be decomposed into three conditions: (i) Coulomb friction on the resultant force, (ii) CoP inside the support area, and (iii) upper and lower bounds on the yaw torque. While the first two are well-known, the third one is novel. It can, for instance, be used to prevent the undesired foot yaws observed in biped locomotion. We show that our formula yields simpler and faster computations than existing approaches for humanoid motions in single support, and assess its validity in the OpenHRP simulator.
Current state of the art techniques have been shown to work in real time with an admirable performance in desktop-size environments. Unfortunately, as we show in this paper, when applied to larger indoor environments, performance often degrades. A common cause is the presence of large affine texture-less areas like by walls, floors, ceilings and drab objects such as chairs and tables. These produce noisy and worse still, grossly erroneous initial seeds for the depth map that greatly impede successful optimisation.
We solve this problem via the introduction of a new non-local higher-order regularisation term that enforces piecewise affine constraints between image pixels that are far apart in the image. This property leverages the observation that the depth at the edges of bland regions are often well estimated whereas their inner pixels are deeply problematic.
A welcome by-product of our proposed technique is an estimate of the surface normals at each pixel. We will show that in terms of implementation, our algorithm is a natural extension of the often used variational approaches. We evaluate the proposed technique using real datasets for which we have ground truth models.
We present a novel multi-objective optimization model for CMOMMT scenarios which features fairness of observation among different targets as an additional objective. The proposed integer linear formulation exploits the available knowledge about the expected motion pattern of the targets. More specifically, for any given future time, a probabilistic occupancy map for each target is estimated in a Bayesian framework.
An empirical analysis of the model is performed in simulation considering multiple scenarios to study the effects of the amount of robots and the prediction accuracy for the mobility of the targets. Both centralized and distributed implementations are presented and compared to each other, evaluating the impact of multi-hop communications and limited information sharing.
This paper proposes a similarity criterion that can be used to solve this problem. It is based on the observation that, if each environment is described in terms of its co-occurrent words, similarity between environments can be established by comparing their co-occurrence matrices. We show that this leads to a novel Place Recognition algorithm, based on the bags of words (BoW) framework, that divides the collection of images into environments and arrange them in a hierarchy of inverted indexes. By selecting first the relevant environment for the operating robot, we can reduce the number of images to perform the actual loop detection, reducing the execution time while preserv
Some part of this manuscript has been submitted to Advanced Robotics and still under review. Different from the manuscript submitted to Advanced Robotics, the authors demonstrate a velocity control based on the MOA setin Sect. VII-C of this manuscript.
When a robot closes a large-scale loop, it must often consider many loop-closure candidates. In this paper, we describe an approach for posing a scanmatching query over these candidates jointly, finding the best match(es) between a particular pose and a set of candidate poses (“one-to-many”), or the best match between two sets of poses (“many-to-many”). This mode of operation finds the first loop closure as much as 45x faster than traditional “one-to-one” scan matching.
Our design utilizes two Stewart-Gough platforms in series, each controlled independently, either in position or force modes. The design can provide controlled forces/torques on different regions of spine to modify the posture. Additionally, it can control the motion of different regions of the spine through independent position control of each platform using six parallel actuators. Both control methods were validated in bench-top tests. A range of motion study was also performed with a healthy subject wearing the device while the system was controlled in transparent mode.
The variable COM height leads to nonlinear ZMP constraints. This work demonstrates that depending on how strict the constraints are, it is possible to generate 1.6 second long trajectories in less than 4 ms on a 3.4 Ghz CPU.
This is done by expressing the problem as a quadratically constrained quadratic program (QCQP) and solving it via sequential quadratic programing (SQP). Computation time is improved by providing the analytical derivatives of all constraints. In particular the nonlinear ZMP constraint is expressed in a quadratic form and its derivative is provided.
Furthermore, and in contrast to existing inverse filters, the proposed IKS's numerical stability allows for efficient 32-bit implementations on resource-constrained devices, such as cell phones and wearables. We validate the IKS for performing vision-aided inertial navigation on Google Glass, a wearable device with limited sensing and processing, and demonstrate positioning accuracy comparable to that achieved on cell phones. To the best of our knowledge, this work presents the first proof-of-concept real-time 3D indoor localization system on a commercial-grade wearable computer.