This paper presents FGLoc6D, a novel approach for robust global localisation and online 6DoF pose estimation of ground robots in forest environments by leveraging deep semantically-guided re-localisation and cross-view factor graph optimisation. The proposed method addresses the challenges of aligning aerial and ground data for pose estimation, which is crucial for accurate point-to-point navigation in GPS-degraded environments. By integrating information from both perspectives into a factor graph framework, our approach effectively estimates the robot’s global position and orientation. Additionally, we enhance the repeatability of deep-learned keypoints for metric localisation in forests by incorporating a semantically-guided regression loss. This loss encourages greater attention to wooden structures, e.g., tree trunks, which serve as stable and distinguishable features, thereby improving the consistency of keypoints and increasing the success rate of global registration, a process we refer to as re-localisation. The re-localisation module along with the factor-graph structure, populated by odometry and ground-to-aerial factors over time, allows global localisation under dense canopies. We validate the performance of our method through extensive experiments in three forest scenarios, demonstrating its global localisation capability and superiority over alternative state-of-the-art in terms of accuracy and robustness in these challenging environments. Experimental results show that our proposed method can achieve drift-free localisation with bounded positioning errors, ensuring reliable and safe robot navigation through dense forests.

Radio connectivity in wireless sensor networks is highly intermittent due to unpredictable and time-varying noise and interference patterns in the environment. Because link qualities are not predictable prior to deployment, current deterministic solutions to unreliable links, such as increasing network density or transmission power, require overprovisioning of network resources and do not always improve reliability. We propose a new dual-radio network architecture to improve communication reliability in wireless sensor networks. Specifically, we show that radio transceivers operating at well-separated frequencies and spatially separated antennas offer robust communication, high link diversity, and better interference mitigation. We derive the optimal parameters for the dual-transceiver setup from frequency and space diversity in theory. We observe that frequency diversity holds the most benefits as long as the antennas are sufficiently separated to prevent coupling. Our experiments on an indoor/outdoor testbed confirm the theoretical predictions and show that radio diversity can significantly improve end-to-end delivery rates and network stability at only a small increase in energy cost over a single radio. Simulation experiments further validate the improvements in multiple topology configurations, but also reveal that the benefits of radio diversity are coupled to the number of available routing paths to the destination.

Usecases for wireless sensor networks, such as building automation or patient care, often collect and transmit sensitive information. Yet, many deployments currently do not protect this data through suitable security schemes. We propose an end-to-end security scheme build upon existing internet standards, specifically the Datagram Transport Layer Security protocol (DTLS). By relying on an established standard existing implementations, engineering techniques and security infrastructure can be reused which enables easy security uptake. We present a system architecture for this scheme and show its feasibility through the evaluation of our implementation.

The characteristics of a variety of 3D lattices are assessed for their performance when applied to typical robotics problems. The lattices studied are the Cubic, Body Centred Cubic (BCC), Face Centred Cubic (FCC), hexagonal prismatic and finally the Mean Centred Cuboidal (MCC). An algorithm for generic quantization to any low dimensional lattice is presented allowing this analysis to be easily extended to other 3D lattices of interest. Tests are undertaken on uniform sampled random data and laser range data from mobile robot platforms including an autonomous skid steer loader. The improvements in accuracy, memory requirements and consistency under rotation for the alternative lattices over the cubic lattice are typically 5-10%. The radial distribution of lattice points is studied through the distribution of points assigned to the same lattice cell and that in neighbouring cells. For instance only 12 neighbouring cells need checking for the FCC lattice as opposed to the cubic lattice which requires 26. Not only are fewer checks required but the distance variation associated with points in adjacent voxels of the FCC lattice is substantially lower than that of a cubic lattice. For the FCC lattice these point distances have a 30% smaller standard deviation and a 40% smaller range. These results make algorithms, such as collision checking and scan/map matching, which often involve many proximity checks, significantly faster and more accurate.

An alternative to the conventional quantization for occupied voxel lists in both 2D and 3D is presented. The performance metrics of the hexagonal lattice in 2D and the face centred and body centred cubic lattices in 3D are investigated and compared to their square and cubic counterparts. It is found that quantization to alternative lattices yields some improvements. Ultimately, the D3 or face centred cubic lattice is highlighted for its lower quantization error, lower rotation variability and higher order rotational symmetry. It has three times less occupied voxel count pose variability than a standard cubic occupied voxel list. These improvements have implications for SLAM and path planning.

We present a Bayesian technique for the reconstruction and subsequent decimation of 3D surface models from noisy sensor data. The method uses oriented probabilistic models of the measurement noise and combines them with feature-enhancing prior probabilities over 3D surfaces. When applied to surface reconstruction, the method simultaneously smooths noisy regions while enhancing features such as corners. When applied to surface decimation, it finds models that closely approximate the original mesh when rendered. The method is applied in the context of computer animation where it finds decimations that minimize the visual error even under nonrigid deformations.

A fast full-search block-matching algorithm is developed. The matching criterion is the sum of absolute differences or the mean-square error. The algorithm evaluates lower bounds for the matching criteria for subdivided blocks in order to reduce the number of search positions. It also uses the lower bounds for a fast calculation of the matching criterion for the remaining search positions. The computational complexity of the algorithm is evaluated and compared to the three-step search strategy. The search result of the algorithm is identical to the search result of the exhaustive search.

In this paper we present a face segmentation algorithm for color images based on connected operators. Using a skin color model, we construct a skin probability image that indicates the probability of each pixel representing skin. Morphological filters are applied to this probability images instead of applying them to the original image. A hierarchy of operators with geometrical criteria (size, compactness, orientation) is employed to simplify the skin probability image and a gray level criterion based on principal components analysis is used for final classification. Using connected operators, regions with different probabilities of being skin are analyzed which leads to a more robust segmentation compared to a single threshold classification.

The second-order statistics of motion-compensated frame differences in a low-bit-rate hybrid video coding scheme with overlapped block motion compensation are investigated. Based on the empirical covariance sequence, an adequate compound covariance model is developed. The prediction gain for motion-compensated frame differences is evaluated, and the performance of the discrete cosine transform for interframe transform coding is discussed.