Point cloud registration is crucial for applications in computer vision and robotics. The Iterative Closest Point (ICP) algorithm has been a key, but limited, solution for this problem, with subsequent methods having been devised to address occlusions and variable point overlap. To cope with detection errors, the Particle Swarm Optimization (PSO)-Cardinalized Optimal Linear Assignment (COLA) algorithm was introduced, providing robustness against point data missed detections and false alarms showing significant performance improvements in data sets with limited overlap. However, since PSO is based on particle swarm optimization, it can be affected by local minima problems. To mitigate these issues, the Artificial Rabbit Optimization (ARO)-COLA algorithm is used in this article, incorporating the ARO approach together with the COLA metric. In this paper, the ARO-COLA and PSO-COLA algorithms will be applied to the challenging outdoor “Wood Summer” dataset, comparing their performances with other state-of-the-art methods. The results will show that with such unstructured data sets, the ARO-COLA registration algorithm outperforms most state-of-the-art registration methods, achieving similar accuracy to its PSO-COLA registration predecessor, but with improvements in runtime.

This article explores the integration of fully autonomous legged robots in obstacle filled environments, simultaneously addressing the challenges of navigation and control. Despite the potential of legged robots for dynamic tasks, their deployment in complex environments has been hindered by the difficulty of developing effective autonomous control systems. In particular, the motion planning problem is addressed in this article, by formulating it as a Partially Observable Markov Decision Process (POMDP) and applying Proximal Policy Optimization (PPO), a model-free Deep Reinforcement Learning (DRL) algorithm. To improve sample efficiency and real-world applicability, the proposed method incorporates a Central Pattern Generator (CPG) for motion planning and a Variational Autoencoder (VAE) for terrain representation, reducing the complexity of action and observation spaces. Referred to as the VAE-CPG architecture, its performance is demonstrated using the Unitree Laikago robot within the PyBullet simulation environment, aiming to show its effectiveness in simulated construction sites. Our findings indicate that by reducing the legged action space to periodic gait patterns and optimizing the gait based on sensory feedback, we achieve enhanced adaptability and efficiency. This work presents a viable means towards the deployment of autonomous legged robots and their improved efficiency in real applications.

In Multiple Extended Object Tracking (MEOT), it is assumed that a solitary target can produce multiple measurements. The quality of these measurements is paramount for obtaining accurate estimates of tracks over time. To test state-of-the-art MEOT algorithms, both simulated and real laser data, recorded in open spaces, have been used. MEOT algorithms work well in these scenarios, but when applied in more cluttered or restricted spaces, they often fail to produce good results, because close proximity target measurements are considered as measurements with the same origin. To address these cases, this article applies the 3D INstance SEGmentation (3D-INSEG) algorithm to MEOT to process stereo image sequences, extracting 3D information corresponding to each detected target using cameras. The algorithm selects pixels from each detected target and calculates the disparity map from stereo pairs, projecting them into 3D space using this disparity map. Subsequently, these measurements undergo processing by an extended target Poisson multi-Bernoulli mixture (PMBM) filter with a gamma Gaussian inverse-Wishart (GGIW) implementation. The advantages of MEOT with the 3D-INSEG-generated data are demonstrated in this article via a comparison with MEOT based on Velodyne LiDAR data points recorded from the same scenario processed by the same MEOT algorithm.

This paper introduces and addresses the implementation of the Multi-Bernoulli Poisson (MBP) filter in multi-target tracking. A performance evaluation in a real scenario, in which a 3D lidar, automotive radar and a video camera are used for tracking people will be provided. For implementation purposes, a Gaussian Mixture (GM) approximation of the MBP filter is used. Comparisons with state of the art GM- δ-GLMB and GM- δ-GMBP filters show similar accuracy, despite the need for less parameters, and therefore less computational cost, within the GM-MBP filter. Further performance improvements of the GM-MBP filter are shown, based on birth intensity and survival distributions, which take into account the common field of view of the sensors and the variation of time steps between asynchronous measurements.

Recently, various algorithms which adopt Random Finite Sets (RFS) for the solution of the fundamental, autonomous robotic, feature based, Simultaneous Localization and Mapping (SLAM) problem, have been proposed. In contrast to their vector based counterparts, these techniques jointly estimate the vehicle and map state and map cardinality. Most of the proposed RFS solutions are based on a Rao-Blackwellized particle filter representing the vehicle state, accompanied by an RFS filter to represent the map. This article shows that an RFS maximum likelihood approach to SLAM is also possible. By maximizing the RFS based measurement likelihood this article demonstrates that Maximum Likelihood (ML) SLAM is possible without the need for external data association algorithms. It will be demonstrated that RFS based ML-SLAM converges to the same solution as its traditional vector-based counterpart. However, fundamentally RFS-ML-SLAM does not require the correct data association decisions necessary for the correct convergence of traditional random vector based approaches.