3D LiDAR-based place recognition has been extensively researched in urban environments, yet it remains underexplored in agricultural settings. Unlike urban contexts, horticultural environments, characterized by their permeability to laser beams, result in sparse and overlapping LiDAR scans with suboptimal geometries. This phenomenon leads to intra-and inter-row descriptor ambiguity. In this work, we address this challenge by introducing SPVSoAP3D, a novel modeling approach that combines a voxel-based feature extraction network with an aggregation technique based on a second-order average pooling operator, complemented by a descriptor enhancement stage. Furthermore, we augment the existing HORTO-3DLM dataset by introducing two new sequences derived from horticultural environments. We evaluate the performance of SPVSoAP3D against state-of-the-art (SOTA) models, including OverlapTransformer, PointNetVLAD, and LOGG3D-Net, utilizing a cross-validation protocol on both the newly introduced sequences and the existing HORTO-3DLM dataset. The findings indicate that the average operator is more suitable for horticultural environments compared to the max operator and other first-order pooling techniques. Additionally, the results highlight the improvements brought by the descriptor enhancement stage. The code is publicly available at https://github.com/Cybonic/SPVSoAP3D.git

This work proposes a novel approach that uses a semantic segmentation mask to obtain a 2D spatial layout of the segmentation-categories across the scene, designated by segmentation-based semantic features (SSFs). These features represent, per segmentation-category, the pixel count, as well as the 2D average position and respective standard deviation values. Moreover, a two-branch network, GS2F2App, that exploits CNN-based global features extracted from RGB images and the segmentation-based features extracted from the proposed SSFs, is also proposed. GS2F2App was evaluated in two indoor scene benchmark datasets: the SUN RGB-D and the NYU Depth V2, achieving state-of-the-art results on both datasets.

Several studies have been published to discuss methods of making components, such as CPUs, GPUs, and FPGAs, more energy efficient. Well-known techniques such as dynamic voltage and frequency scaling (DVFS) and power gating are alternatives since the supply voltage is directly related to power consumption. However, to guarantee correct operation without critical-path-timing violations, the systems must impose conservative static voltage guardbands. We propose to create a predictive guardband reduction technique for CPUs by analyzing the cached instructions. The contribution is expected to be the development of a machine learning solution capable of reducing average supply voltage levels by capturing the intrisic critical path variations according to which internal circuits are used by different sets of instructions.

Convolutional Neural Networks (CNNs) have been increasingly applied in visual classification tasks by replacing hand-crafted features with deep features. However, problems such as inter-class similarity and intra-class variation led to the need of obtaining more descriptive features. To accomplish this, a new semantic inter-object relationship approach is proposed, which is based on the distance relationships between recognized objects. This new source of information represents how close or apart objects belonging to two object classes are, which, together with the number of object occurrences, allows to develop a more descriptive semantic feature representation of the scene. To exploit such semantic features, a two-branch CNN architecture based on 1D and 2D convolutional layers, is proposed. Also, an enhancement version, GSF2AppV2, of the Global and Semantic Feature Fusion described in [1] is proposed by integrating the new semantic inter-object relationship approach, as well as the aforementioned two-branch CNN architecture. The GSF2AppV2 is also composed of a CNN-based global feature branch, where five different CNN-based feature extraction approaches were assessed as global feature extraction modules. Moreover, to combine global and semantic features, two feature fusion approaches are proposed and evaluated: correlation and triple concatenation. GSF2AppV2 was evaluated in two benchmark datasets: the SUN RGB-D and NYU Depth V2. State-of-the-art results were achieved on both datasets, showing the effectiveness of the proposed semantic feature approach on the pipeline.

This paper presents a study in terms of accuracy and inference speed using RGB-D object detection and classification for mobile platform applications. The study is divided in three stages. In the first, eight state-of-the-art CNN-based object classifiers (AlexNet, VGG16-19, ResNet1850-101, DenseNet, and MobileNetV2) are used to compare the attained performances with the corresponding inference speeds in object classification tasks. The second stage consists in exploiting YOLOv3/YOLOv3-tiny networks to be used as Region of Interest generator method. In order to obtain a real-time object recognition pipeline, the final stage unifies the YOLOv3/YOLOv3-tiny with a CNN-based object classifier. The pipeline evaluates each object classifier with each Region of Interest generator method in terms of their accuracy and frame rate. For the evaluation of the proposed study under the conditions in which real robotic platforms navigate, a nonobject centric RGB-D dataset was recorded in Institute of Systems and Robotics facilities using a camera on-board the ISR-InterBot mobile platform. Experimental evaluations were also carried out in Washington and COCO datasets. Promising performances were achieved by the combination of YOLOv3tiny and ResNet18 networks on the embedded hardware Nvidia Jetson TX2.

Robot localization in indoor environments is crucial for achieving flexible automated navigation. In this paper, we propose a novel multi-stage localization approach for mobile robots which combines a commercial beacon-based absolute indoor positioning system with laser scan data. This configuration of sensors can be deployed in a non-intrusive way in most robotic platforms without the use of proprioceptive sensors (e.g. wheel encoders) which often introduce non-negligible maintenance and downtime costs. The data fusion is performed by a particle filter aided by a refinement stage. The proposed approach was evaluated in two different indoor scenarios with a mobile platform equipped with a mobile Marvelmind beacon and a Hokuyo UTM-30LX scanning laser rangefinder. The proposed localization framework, purposely without proprioceptive data, is compared with an AMCL approach having as inputs odometry, calculated from wheel encoders' data, and laser scan data. Preliminary results show that the proposed approach can provide an accurate localization estimate and that using the refinement stage improves localization.

This paper introduces a new mobile robot localization solution consisting of two main modules: a Particle-Filter based Localization (PFL) and a Reinforcement-Learning based map updating, integrating relative measurements and absolute indoor positioning sensor (A-IPS) data. Concerning localization using 2D-LiDARs, featureless areas are known to be problematic. To solve this problem a classic PFL approach was modified to incorporate A-IPS position measurements in the prediction and update stages. The localization approach has the particularity of including the possibility of updating the map whenever major modifications are detected in the environment in relation to the current localization map. Due to the random sampling-based nature of the PFL, an associated map update solution is not trivial since small inconsistencies in the estimated pose can lead to erroneous map associations. The proposed method learns to decide by assigning higher rewards the greater is the overlap between the map and the 2DLIDAR scans, via RL, and then a proper update of the map is achieved. Validation of the proposed pipeline was carried out in a differential drive platform with algorithms developed in ROS. Tests were performed in two scenarios in order to assess the performance of both the localization module and the map update stage. The results show that the proposed localization method offers improvements in relation to known approaches, and consequently suggest promising perspectives for the proposed map update decision framework.