检索结果-内蒙古大学图书馆

KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Guadagnino, Tiziano Mersch, Benedikt Gupta, Saurabh Vizzo, Ignacio Grisetti, Giorgio Stachniss, Cyrill Center for Robotics University of Bonn Germany Sapienza University of Rome Italy Department of Engineering Science the University of Oxford United Kingdom Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later, advanced systems further improved the estimation at the cost of a higher runtime and complexity. This paper explores the limits of what can be achieved with a LiDAR-only SLAM approach while following the "Keep It Small and Simple" (KISS) principle. By leveraging this minimalistic design principle, our system, KISS-SLAM, archives state-of-the-art performances in pose accuracy while requiring little to no parameter tuning for deployment across diverse environments, sensors, and motion profiles. We follow best practices in graph-based SLAM and build upon LiDAR odometry to compute the relative motion between scans and construct local maps of the environment. To correct drift, we match local maps and optimize the trajectory in a pose graph optimization step. The experimental results demonstrate that this design achieves competitive performance while reducing complexity and reliance on additional sensor modalities. By prioritizing simplicity, this work provides a new strong baseline for LiDAR-only SLAM and a high-performing starting point for future research. Further, our pipeline builds consistent maps that can be used directly for further downstream tasks like navigation. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot applications

Comparison of Approaches for Human Detection with Low-Resolution Infrared Data Sets Using Deep learning

学校读者我要写书评

暂无评论

Comparison of Approaches for Human Detection with Low-Resolu...

International Conference on Ubiquitous Robots (UR)

作者： Damian Läufer Simone Braun Sinan Süme Urban Himmelsbach Department Business Institute for Machine Learning and Analytics Offenburg University of Applied Sciences Gengenbach Germany Department Business Work-Life Robotics Institute Offenburg University of Applied Sciences Offenburg Germany

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

Human-machine interaction can be supported by the detection of humans through the simultaneous localization and distinction from non-human objects. This paper compares modern object detection algorithms (Damo-YOLO, YOLOv6, YOLOv7 and YOLOv8) in combination with Transfer learning and Super Resolution in different scenarios to achieve human detection on low resolution infrared images. The data set created for this purpose includes images of an empty room, images of warm coffee cups, and images of people in various scenarios and at distances ranging from two to six meters. The Average Precision AP@50 and AP@50:95 values achieved across all scenarios reach up to 98.02 % and 66.99 % respectively.

关键词： Meters Location awareness Human computer interaction Deep learning Transfer learning Superresolution Object detection

Generative-Contrastive learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wu, Chengzhi Pfrommer, Julius Zhou, Mingyuan Beyerer, Jürgen Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Germany Fraunhofer Center for Machine Learning Germany

We propose a combined generative and contrastive neural architecture for learning latent representations of 3D volumetric shapes. The architecture uses two encoder branches for voxel grids and multi-view images from the same underlying shape. The main idea is to combine a contrastive loss between the resulting latent representations with an additional reconstruction loss. That helps to avoid collapsing the latent representations as a trivial solution for minimizing the contrastive loss. A novel switching scheme is used to cross-train two encoders with a shared decoder. The switching scheme also enables the stop gradient operation on a random branch. Further classification experiments show that the latent representations learned with our self-supervised method integrate more useful information from the additional input data implicitly, thus leading to better reconstruction and classification performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Guadagnino, Tiziano Mersch, Benedikt Vizzo, Ignacio Gupta, Saurabh Malladi, Meher V.R. Lobefaro, Luca Doisy, Guillaume Stachniss, Cyrill Center for Robotics University of Bonn Germany Dexory United Kingdom Department of Engineering Science The University of Oxford United Kingdom Lamarr Institute for Machine Learning and Artificial Intelligence Germany

LiDAR odometry is essential for many robotics applications, including 3D mapping, navigation, and simultaneous localization and mapping. LiDAR odometry systems are usually based on some form of point cloud registration to compute the ego-motion of a mobile robot. Yet, few of today’s LiDAR odometry systems consider domain-specific knowledge or the kinematic model of the mobile platform during the point cloud alignment. In this paper, we present Kinematic-ICP, a LiDAR odometry system that focuses on wheeled mobile robots equipped with a 3D LiDAR and moving on a planar surface, which is a common assumption for warehouses, offices, hospitals, etc. Our approach introduces kinematic constraints within the optimization of a traditional point-to-point iterative closest point scheme. In this way, the resulting motion follows the kinematic constraints of the platform, effectively exploiting the robot’s wheel odometry and the 3D LiDAR observations. We dynamically adjust the influence of LiDAR measurements and wheel odometry in our optimization scheme, allowing the system to handle degenerate scenarios such as feature-poor corridors. We evaluate our approach on robots operating in large-scale warehouse environments, but also outdoors. The experiments show that our approach achieves top performances and is more accurate than wheel odometry and common LiDAR odometry systems. Kinematic-ICP has been recently deployed in the Dexory fleet of robots operating in warehouses worldwide at their customers’ sites, showing that our method can run in the real world alongside a complete navigation stack. Copyright © 2024, The Authors. All rights reserved.

关键词： Wheels

AdaCropFollow: Self-Supervised Online Adaptation for Visual Under-Canopy Navigation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Sivakumar, Arun N. Magistri, Federico Gasparino, Mateus V. Behley, Jens Stachniss, Cyrill Chowdhary, Girish IL United States Center for Robotics University of Bonn Germany Department of Engineering Science The University of Oxford United Kingdom Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Under-canopy agricultural robots can enable various applications like precise monitoring, spraying, weeding, and plant manipulation tasks throughout the growing season. Autonomous navigation under the canopy is challenging due to the degradation in accuracy of RTK-GPS and the large variability in the visual appearance of the scene over time. In prior work, we developed a supervised learning-based perception system with semantic keypoint representation and deployed this in various field conditions. A large number of failures of this system can be attributed to the inability of the perception model to adapt to the domain shift encountered during deployment. In this paper, we propose a self-supervised online adaptation method for adapting the semantic keypoint representation using a visual foundational model, geometric prior, and pseudo labeling. Our preliminary experiments show that with minimal data and fine-tuning of parameters, the keypoint prediction model trained with labels on the source domain can be adapted in a self-supervised manner to various challenging target domains onboard the robot computer using our method. This can enable fully autonomous row-following capability in under-canopy robots across fields and crops without requiring human intervention. © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Opra, Balázs Dem, Betty Le Walls, Jeffrey M. Lukarski, Dimitar Stachniss, Cyrill Toyota Inc. Balázs Opra Germany The University of Bonn Germany The Center for Robotics University of Bonn Germany The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This paper addresses the challenge of road graph construction for autonomous vehicles. Despite recent advances, creating a road graph remains labor-intensive and has yet to achieve full automation. The goal of this paper is to generate such graphs automatically and accurately. Modern cars are equipped with onboard sensors used for today’s advanced driver assistance systems like lane keeping. We propose using global navigation satellite system (GNSS) traces and basic image data acquired from these standard sensors in consumer vehicles to estimate road-level maps with minimal effort. We exploit the spatial information in the data by framing the problem as a road centerline semantic segmentation task using a convolutional neural network. We also utilize the data’s time series nature to refine the neural network’s output by using map matching. We implemented and evaluated our method using a fleet of real consumer vehicles, only using the deployed onboard sensors. Our evaluation demonstrates that our approach not only matches existing methods on simpler road configurations but also significantly outperforms them on more complex road geometries and topologies. This work received the 2023 Woven by Toyota Invention Award. © 2024, CC BY-NC-ND.

关键词： Semantic Segmentation

Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Point Clouds

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Fusaro, Daniel Magistri, Federico Behley, Jens Pretto, Alberto Stachniss, Cyrill The University of Padua Italy The Center for Robotics University of Bonn Germany The Department of Engineering Science The University of Oxford United Kingdom The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Robotic fruit monitoring is a key step toward automated agricultural production systems. Robots can significantly enhance plant and temporal fruit monitoring by providing precise, high-throughput assessments that overcome the limitations of traditional manual methods. Fruit monitoring is a challenging task due to the significant variation in size, shape, orientation, and occlusion of fruits. Also, fruits may be harvested or newly grown between recording sessions. Most methods are 2D image-based and they lack the 3D structure, depth, and spatial information, which represent key aspects of fruit monitoring. 3D colored point clouds, instead, can offer this information but they introduce challenges such as their sparsity and irregularity. In this paper, we present a novel approach for temporal fruit monitoring that addresses point clouds collected in a greenhouse over time. Our method segments fruits using a learning-based instance segmentation approach directly on the point cloud. Each segmented fruit is processed by a 3D sparse convolutional neural network to extract descriptors, which are used in an attention-based matching network to associate fruits with their instances from previous data collections. Experimental results on a real dataset of strawberries demonstrate that our approach outperforms other methods for fruits re-identification over time, allowing for precise temporal fruit monitoring in real and complex scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Fruits

PLAN-BASED RELAXED REWARD SHAPING FOR GOAL-DIRECTED TASKS 9

学校读者我要写书评

暂无评论

PLAN-BASED RELAXED REWARD SHAPING FOR GOAL-DIRECTED TASKS

9th International Conference on learning Representations, ICLR 2021

作者： Schubert, Ingmar Oguz, Ozgur S. Toussaint, Marc Learning and Intelligent Systems Group TU Berlin Germany Max Planck Institute for Intelligent Systems Stuttgart Germany Machine Learning and Robotics Lab University of Stuttgart Germany

In high-dimensional state spaces, the usefulness of Reinforcement learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward shaping (PB-RS) previously. In the present work, we introduce Final-Volume-Preserving Reward Shaping (FV-RS). FV-RS relaxes the strict optimality guarantees of PB-RS to a guarantee of preserved long-term behavior. Being less restrictive, FV-RS allows for reward shaping functions that are even better suited for improving the sample efficiency of RL algorithms. In particular, we consider settings in which the agent has access to an approximate plan. Here, we use examples of simulated robotic manipulation tasks to demonstrate that plan-based FV-RS can indeed significantly improve the sample efficiency of RL over plan-based PB-RS. © 2021 ICLR 2021 - 9th International Conference on learning Representations. All rights reserved.

关键词： Reinforcement learning

Balanced Pedestrian Attribute Recognition for Improved Attribute-based Person Retrieval

学校读者我要写书评

暂无评论

Balanced Pedestrian Attribute Recognition for Improved Attri...

Pattern Recognition Systems (ICPRS), International Conference on

作者： Andreas Specker Jürgen Beyerer Fraunhofer IOSB Karlsruhe Germany Vision and Fusion Lab Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Fraunhofer Center for Machine Learning

Attribute-based person retrieval is a crucial task in surveillance systems since it enables the search for an individual if no image of the suspect is available. Searches can be based on soft biometric characteristics, understood as semantic attributes people typically use to describe each other. Examples are gender, age, or a description of the person's clothing and accessories. One possible solution is Pedestrian Attribute Recognition (PAR). Resulting attribute predictions are compared to the query attributes with the model's predictions. However, related literature indicates approaches tend to either perform well regarding the recognition of single attributes or achieve superior retrieval results at the expense of an increased number of false positives. This work aims to improve single-attribute recognition and attribute-based retrieval simultaneously by applying spatial projection and normalization modules. Spatial projection extracts features for different receptive fields to account for global as well as local attributes. Our PARNorm module normalizes attribute logits attribute-and instance-wise to enhance label-based PAR metrics without hurting instance-based performance. Furthermore, we propose a novel evaluation metric for attribute-based person retrieval that considers the degree of match between attribute queries and gallery images instead of using binary relevance labels. This allows for more realistic estimates of retrieval accuracy, which correlate strongly with the visual impression of the rankings. Our model outperforms the current state-of-the-art in generalization on the UPAR dataset and its challenge version.

关键词：