Simultaneous localization and mapping (SLAM) deals with localizing and mapping in unknown environment. Semantic SLAM incorporates an additional layer of objects identities and their relationships. Here we suggest representing the identity of an object in semantic SLAM as a probability distribution over the object's traits, such as labels, colors, shapes, materials, etc. Objects' identities are estimated by integrating measurements from different sensors and are distinguished based on the discrepancy between the underlying probability distributions as quantified by the Bhattacharyya distance. The semantic mapping scheme is tested both in simulation and experiment using a ground robot.

Relying on the idea of importance sampling for substantiating the Bayesian filtering recursion, particle filters may become prohibitively inefficient even for moderate state dimensions and likewise whenever the signal to noise ratio is relatively high, as is the case with nearly deterministic state dynamics or random parameters. Markov chain Monte Carlo particle filters completely avoid importance sampling and by that circumvent many of the deficiencies associated with conventional particle filters. These methods may nevertheless suffer from slow convergence rate once inadequate or computationally intractable proposal distributions are used for generating new candidate samples in the underlying Markov chain. In this work, we devise a new Markov chain Monte Carlo particle filter whose sampling mechanism employs jumping Gaussian distributions. This technique enhances the underlying sampling efficiency and leads to significant reduction in the computational cost. The newly derived filter is shown to outperform the conventional (regularised) particle filter both in terms of accuracy and computational overhead, particularly when applied to estimation in systems with low intensity noise or of relatively high state dimensions.

A technique is presented for combining arbitrary empirical probability density estimates whose interdependencies are unspecified. The underlying estimates may be, for example, the particle approximations of a pair of particle filters. In this respect, our approach, named hereafter particles intersection, provides a way to obtain a new particle approximation, which is better in a precise information-theoretic sense than that of any of the particle filters alone. Particles intersection is applicable in networks with potentially many particle filters. We demonstrate both theoretically and through numerical simulations that depending on the communication topology this technique leads to consensus in the underlying network where all particle filters agree on their estimates. The viability of the proposed approach is demonstrated through examples in which it is applied for multiple object tracking and distributed estimation in networks.

A technique is presented for combining arbitrary empirical probability density estimates whose interdependencies are unspecified. The underlying estimates may be, for example, the particle approximations of a pair of particle filters. In this respect, our approach provides a way to obtain a new particle approximation, which is better in a precise information-theoretic sense than that of any of the particle filters alone. The viability of the proposed approach is demonstrated in a multiple object tracking scenario.

In this paper, we present the swing-up regrasping problem in which an object is manipulated using a robotic arm around a point pinched by the arm's gripper. The aim of the regrasping is to manipulate the object from an initial angle to regrasp it on a new desired angle relative to the gripper. The pinching point functions as a semiactive joint at which the gripper is able to apply only frictional torques on the object to resist its motion. We address the problem by proposing a novel approach that incorporates an impulse-momentum method with a clipped linear quadratic regulator (cLQR) based controller for stabilization on the desired angle. In particular, a suboptimal cLQR controller is presented to deal with the dissipative semiactive joint. The interaction of these methods with the unique property of the semiactive joint is investigated and analyzed. Simulations on a six degrees of freedom manipulator regrasping a bottle validate the proposed approach. Moreover, a full experiment was conducted on a robotic arm to test the approach and the control of a semiactive joint. The simulations and experiment have proven the feasibility of the method.