Despite having fewer neurons than more complex life forms, insects are still capable of producing astonishing locomotive behaviors, such as traversing diverse environments and making rapid gait adaptations after extreme injury or autotomy. Biologists attribute this to a chain of segmental neuron clusters (ganglia) within insect nervous systems, which act as distributed self-organizing sensorimotor control units. Inspired by the neural structure of the Carausius morosus, the common stick insect, this work introduces the Distributed Neural Locomotion Controller (D-NLC), a modular control framework that utilizes local proprioceptive feedback to modulate joint-level Central Pattern Generator (CPG) signals to produce emergent locomotive behaviors. This framework was implemented on a modular legged robot with distributed jointlevel embedded computing units. In addition, assessments were conducted on the framework's performance and behavior in various experimental settings. Based on real-world experiments, we observe an overall 31.3% average increase in curvilinear motion performance under external (terrain) and internal (amputation) perturbation compared to a centralized predefined gait controller. This difference is statistically significant$(P \ll 0.05)$for larger perturbations but not for single-leg amputations. Experiments with perturbation-induced leg stance duration and leg phase-difference analysis further validated our hypothesis regarding D-NLC's role in the robust perceptive locomotion and self-emergent gait adaptation against complex unforeseen perturbations. This proposed control framework does not require any numerical optimization or weight training processes, which are time-consuming and computationally expensive. To the best of our knowledge, this framework is the first bio-inspired neural controller deployed on a distributed embedded system. More info at https://eigenbot-dnlc.github.io.

Loop closure is critical in Simultaneous Localization and Mapping (SLAM) systems to reduce accumulative drift and ensure global mapping consistency. However, conventional methods struggle in perceptually aliased environments, such as narrow pipes, due to vector quantization, feature sparsity, and repetitive textures, while existing solutions often incur high computational costs. This paper presents Bag-of-Word-Groups (BoWG), a novel loop closure detection method that achieves superior precision-recall, robustness, and computational efficiency. The core innovation lies in the introduction of word groups, which captures the spatial co-occurrence and proximity of visual words to construct an online dictionary. Additionally, drawing inspiration from probabilistic transition models, we incorporate temporal consistency directly into similarity computation with an adaptive scheme, substantially improving precision-recall performance. The method is further strengthened by a feature distribution analysis module and dedicated post-verification mechanisms. To evaluate the effectiveness of our method, we conduct experiments on both public datasets and a confined-pipe dataset we constructed. Results demonstrate that BoWG surpasses state-of-the-art methods—including both traditional and learning-based approaches—in terms of precision-recall and computational efficiency. Our approach also exhibits excellent scalability, achieving an average processing time of 16 ms per image across 17,565 images in the Bicocca25b dataset. The source code is available at: https://github.com/EdgarFx/BoWG.

In deep metric learning, the triplet loss has emerged as a popular method to learn many computer vision and natural language processing tasks such as facial recognition, object detection, and visual-semantic embeddings. One issue that plagues the triplet loss is network collapse, an undesirable phenomenon where the network projects the embeddings of all data onto a single point. Researchers predominately solve this problem by using triplet mining strategies. While hard negative mining is the most effective of these strategies, existing formulations lack strong theoretical justification for their empirical success. In this paper, we utilize the mathematical theory of isometric approximation to show an equivalence between the triplet loss sampled by hard negative mining and an optimization problem that minimizes a Hausdorff-like distance between the neural network and its ideal counterpart function. This provides the theoretical justifications for hard negative mining's empirical efficacy. Experiments performed on the Market-1501 and Stanford Online Products datasets with various network architectures corroborate our theoretical findings, indicating that network collapse tends to happen when batch size is too large or embedding dimension is too small. In addition, our novel application of the isometric approximation theorem provides the groundwork for future forms of hard negative mining that avoid network collapse.

Increased usage of additive manufacturing (AM) in various industries has solidified its role as an advanced manufacturing technique. However, there is an inherent lack of reliability in AM processes, particularly common in extrusion or deposition-based methods due to the stochastic nature of ma-terial deposition. This necessitates an intelligent manufacturing solution to address the drawbacks of AM. Thus, we propose a novel layer-wise approach toward closed-loop AM, which is capable of in-situ monitoring and repairing geometric defects. In this paper, we present a system that uses a robotic AM experimental platform that mimics a conventional open-loop fabrication setup, which we augment into a closed-loop system using two add-ons: in-situ inspection subsystem and online process correction subsystem. The in-situ inspection subsystem collects 3D point cloud scans and compares them against a reference CAD model, categorizing geometric deviations as positive or negative defects. Then the subsequent online process correction subsystem uses a re-plan and/or repair strategy to address the positive and/or negative defects, respectively. To evaluate this idea, we conducted three experiments on parts with manually induced defects to investigate the system's ability to repair those parts, thereby reducing defects, improving part accuracy, and enhancing mechanical properties. Comparing the defective and repaired parts, we observe a reduction in defect percent by volume from 10.7% to 1.3%, an improvement in geometric tolerance from 3.86% error to 0.08% error, and an increase in the part's breaking load from 4.77 kN to 6.31 kN. These experiments prove that our layer-wise closed-loop additive manufacturing approach improves the quality, tolerance, and reliability of plastic 3D printed parts, with the potential to extend to other extrusion/deposition-based AM processes, or even subtractive manufacturing and hybrid manufacturing methods.

Robotic solutions for pipeline inspection promise enhancement of human labor by automating data acquisition for pipe condition assessments, which are vital for the early detection of pipe anomalies and the prevention of hazardous leakages and explosions. Through simultaneous localization and mapping (SLAM), colorized 3D reconstructions of the pipe's inner surface can be generated, providing a more comprehensive digital record of the pipes compared to conventional vision-only inspection. Designed for generic environments, most SLAM methods suffer limited accuracy and substantial accumulative drift in confined and featureless spaces such as pipelines, due to a lack of suitable sensor hardware and state estimation techniques. In this research, we present VILL-SLAM: a dense RGB-D SLAM algorithm that combines a monocular camera (V), an inertial sensor (I), a ring-shaped laser profiler (L), and a Lidar (L) into a compact sensor package optimized for in-pipe operations. By fusing complementary visual and depth information from the color camera, laser profiling, and Lidar measurement, our method overcomes the challenges of metric scale mapping in conventional SLAM methods, despite its monocular configuration. To further improve localization accuracy, we utilize the pipe geometry to formulate two unique optimization factors that effectively constrain odometer drift. To validate our method, we conducted real-world experiments in physical pipes, comparing the performance of our approach against other state-of-the-art algorithms. The proposed SLAM framework achieved 6.6 times drift improvement with 0.84% mean odometry drift over 22 meters and a mean pointwise 3D scanning error of 0.88mm in 12-inch diameter pipes. This research represents a significant advancement in miniature in-pipe inspection, localization, and mapping sensing techniques. It has the potential to become a core enabling technology for the next generation of highly capable in-pipe robots, capable of reconstructing photo-realistic 3D pipe scans and providing disruptive pipe locating and georeferencing capabilities.

This paper presents a Video Inpainting algorithm that enables monocular-camera-laser-based pipeline inspection robots to capture both color and 3D information using only one video stream. Conventional monocular-camera-laser inspection methods are limited to capture either 2D color images or 3D point clouds since the laser tends to overexpose the actual color of the scanning area. We propose a real-time Video Inpainting method to solve this problem with minimal hardware needs that can be easily integrated with conventional pipeline profiling robots. The algorithm is accelerated by two components: a lightweight network that directly predicts the complete optical flow and simplifies the algorithm pipeline, and the Polar coordinate transformation, which significantly reduces the image processing compexity. Real-world experiments demonstrate that our online algorithm has comparable or better color estimation accuracy against state-of-the-art offline algorithms, while is capable of running at 23 frames per second (FPS) on a laptop computer with a resolution of 1024 × 1024 pixels. In addition, we verify that this method can be used for video pre-processing for downstream tasks that require high-quality visual inputs, such as Simultaneously Localization and Mapping (SLAM). To the best of our knowledge, this is the first real-time Video Inpainting algorithm that can be used for in-pipe environments, serving as an important building block for highly compact RGB-D inspection sensors and robots for the pipeline industry.

Tactile sensing typically involves active exploration of unknown surfaces and objects, making it especially effective at processing the characteristics of materials and textures. A key property extracted by human tactile perception in material classification is surface roughness, which relies on measuring vibratory signals using the multi-layered fingertip structure. Existing robotic systems lack tactile sensors that are able to provide high dynamic sensing ranges, perceive material properties, and maintain a low hardware cost. In this work, we introduce the reference design and fabrication procedure of a miniature and low-cost tactile sensor consisting of a biomimetic cutaneous structure, including the artificial fingerprint, dermis, epidermis, and an embedded magnet-sensor structure which serves as a mechanoreceptor for converting mechanical information to digital signals. The presented sensor is capable of detecting high-resolution magnetic field data through the Hall effect and creating high-dimensional time-frequency domain features for material texture classification. Additionally, we investigate the effects of different superficial sensor fingerprint patterns for classifying materials through both simulation and physical experimentation. After extracting time series and frequency domain features, we assess a k-nearest neighbors classifier for distinguishing between different materials. The results from our experiments show that our biomimetic tactile sensors with fingerprint ridges can classify materials with more than 7.7% higher accuracy and lower variability than ridge-less sensors. These results, along with the low cost and customizability of our sensor, demonstrate high potential for lowering the barrier to entry for a wide array of robotic applications, including modelless tactile sensing for texture classification, material inspection, and object recognition.

Three-dimensional reconstruction in confined spaces is important for the manufacturing of aircraft wings, inspection of narrow pipes, examination of turbine blades, etc. It is also challenging because confined spaces tend to lack a positioning infrastructure, and conventional sensors often cannot detect objects in close range. Therefore, such tasks require a sensor that is compact, operates in short-range, and able to localize itself. In this paper, we introduce a miniature and low-cost 3D scanning system including an active laser-stripe triangulation hardware, integrated inertial sensors, and a Simultaneous Localization and Mapping (SLAM) software tailored for the sensor. The proposed system is capable of reconstructing photo-realistic 3D point cloud in real-time in spite of its compact monocular configuration. To achieve this capability, we propose an approach to capture both color and geometry using alternating shutter-speed on a single camera. A novel SLAM method is proposed to accurately localize the sensor by fusing laser, camera, and inertial measurements. Evaluation of localization accuracy and comparison on reconstruction performance against a significantly larger commercial off-the-shelf sensor demonstrate the proposed system’s advantages in real-world applications.

Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieve long-term localization under varying environmental conditions and changing viewpoints. SeqSLAM uses a brute-force sequential matching method, which is computationally intensive. In this work, we introduce a multi-resolution sampling-based global visual place recognition method (MRS-VPR), which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence is over a much smaller time scale than the reference sequence. Our experiments demonstrate that MRSVPR is efficient in locating short temporary trajectories within long-term reference ones without compromising on the accuracy compared to SeqSLAM.

Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the environmental condition-related features from those that are not. The only label required within this feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant environmental conditions.

Place recognition is one of the major challenges for the LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global geometry map need to be restored. In this paper, we accomplish the place recognition task based on an end-to-end feature learning framework with the LiDAR inputs. This method consists of two core modules, a dynamic octree mapping module that generates local 2D maps with the consideration of the robot's motion; and an unsupervised place feature learning module which is an improved adversarial feature learning network with additional assistance for the long-term place recognition requirement. More specially, in place feature learning, we present an additional Generative Adversarial Network with a designed Conditional Entropy Reduction module to stabilize the feature learning process in an unsupervised manner. We evaluate the proposed method on the Kitti dataset and North Campus Long-Term LiDAR dataset. Experimental results show that the proposed method outperforms state-of-the-art in place recognition tasks under long-term applications. What's more, the feature size and inference efficiency in the proposed method are applicable in real-time performance on practical robotic platforms.

This work presents the design and evaluation of a low-cost tri-axial force sensor, that has been developed to regain the sense of touch in minimally invasive surgeries (MIS). The force sensor uses an array of force sensitive resistors (FSR) with a mechanically pre-loaded structure to perform the force sensing. The sensor has a built-in signal conditioning circuitry to provide on-board power regulation, programmable signal amplification and analog to digital conversion. The sensor is inexpensive and highly sensitive to low-amplitude force, critical in surgical applications. We validate the efficacy of the sensor with two surgical applications - robotic palpation for stiffness mapping and obstacle avoidance for a highly articulated robotic probe (HARP). The results show that the sensor is capable of accurately detecting the stiff inclusions embedded in the tissues as well as detecting obstacles and helping HARP safely navigate around them.