Efficient and Robust Semantic Mapping for Indoor Environments

Seichter, Daniel; Langer, Philipp; Wengefeld, Tim; Lewandowski, Benjamin; Hochemer, Dominik; Groß, Horst–Michael

doi:10.1109/icra46639.2022.9812205

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Other approaches to low-level semantic mapping produce metric maps capturing object information [29], [30]. Most similar to our method, a variety of approaches begin by performing low-level mapping (labeling objects), then back out high-level region categories [1]- [5], [31]- [33]. In this work, we instead generate a region map online without explicitly representing low-level information.…”

Section: Related Workmentioning

confidence: 99%

Focus on Impact: Indoor Exploration With Intrinsic Motivation

Bigazzi¹,

Landi²,

Cascianelli³

et al. 2022

IEEE Robot. Autom. Lett.

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Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments. In the literature, there has been an extensive focus on object labeling and exhaustive scene graph generation; less effort has been focused on the task of purely identifying and mapping large semantic regions. The present work proposes a method for semantic region mapping via embodied navigation in indoor environments, generating a high-level representation of the knowledge of the agent. To enable region identification, the method uses a vision-tolanguage model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location. This mapping procedure is paired with a trained navigation policy to enable autonomous map generation. The proposed method significantly outperforms a variety of baselines, including an object-based system and a pretrained scene classifier, in experiments in a photorealistic simulator.

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Section: Related Workmentioning

confidence: 99%

Focus on Impact: Indoor Exploration With Intrinsic Motivation

Bigazzi¹,

Landi²,

Cascianelli³

et al. 2022

IEEE Robot. Autom. Lett.

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“…Each configuration may yield disparate outcomes, underscoring the necessity to rigorously evaluate typical multi-task balancing techniques within each specific setup, encompassing the tasks involved, dataset characteristics, and model complexity (Standley et al, 2019;Zamir et al, 2020). As highlighted by Seichter et al (2020) in their work on multitask learning for person detection, posture classification, and orientation estimation, balancing the tasks becomes harder the more heterogeneous the tasks are, e.g., mixing both regression and classification tasks. Various works underscore the importance of physical testing, as relying solely on previous works may overlook unique aspects of the current configuration, thereby potentially leading to inaccurate or sub-optimal results (Kendall et al, 2017;Liu et al, 2019).…”

Section: Multi-task Learning In Roboticsmentioning

confidence: 99%

Efficient human 3D localization and free space segmentation for human-aware mobile robots in warehouse facilities

Arapis,

Jami,

Nalpantidis

2023

Front. Robot. AI

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Real-time prediction of human location combined with the capability to perceive obstacles is crucial for socially-aware navigation in robotics. Our work focuses on localizing humans in the world and predicting the free space around them by incorporating other static and dynamic obstacles. We propose a multi-task learning strategy to handle both tasks, achieving this goal with minimal computational demands. We use a dataset captured in a typical warehouse environment by mounting a perception module consisting of a Jetson Xavier AGX and an Intel L515 LiDAR camera on a MiR100 mobile robot. Our method, which is built upon prior works in the field of human detection and localization demonstrates improved results in difficult cases that are not tackled in other works, such as human instances at a close distance or at the limits of the field of view of the capturing sensor. We further extend this work by using a lightweight network structure and integrating a free space segmentation branch that can independently segment the floor space without any prior maps or 3D data, relying instead on the characteristics of the floor. In conclusion, our method presents a lightweight and efficient solution for predicting human 3D location and segmenting the floor space for low-energy consumption platforms, tested in an industrial environment.

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“…In contrast, NDT, a probabilistic method, reduces environmental discrepancies. It also achieves rapid, centimeter-level accuracy with optimal grid sizes [9] . Intelligent vehicles, even on designated routes, face obstacles.…”

Section: Introductionmentioning

confidence: 99%

Obstacle detection method based on point cloud registration and target reduction processing efficient detection of vehicle obstacles

Pi,

Guan,

Wang

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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Obstacle detection systems serve as a linchpin in ensuring the safety of autonomous vehicles. While contemporary detection techniques grapple with intricate results, diminished efficiency, and substantial computational burdens, our research introduces a groundbreaking method anchored in point cloud registration and target reduction for areas with obstructions. This approach not only facilitates high-precision positioning but also expedites the process of obstacle detection, a cornerstone for the subsequent navigation and obstacle evasion tasks in intelligent vehicles. Initially, we harness the power of NDT registration to achieve meticulous positioning. Subsequent stages involve refining the detection landscape by filtering out non-road obstacles using our innovative target reduction process and then employing advanced clustering techniques to pinpoint obstacles. We've validated our approach using the comprehensive Mulran dataset. Both qualitative and quantitative assessments underscore the method's robustness, reliability, and superior effectiveness. Experimental outcomes compellingly demonstrate that our newly proposed obstacle detection framework considerably elevates both operational efficiency and accuracy, paving the way for safer and smarter autonomous driving.

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Efficient and Robust Semantic Mapping for Indoor Environments

Cited by 9 publications

References 26 publications

Focus on Impact: Indoor Exploration With Intrinsic Motivation

Focus on Impact: Indoor Exploration With Intrinsic Motivation

Efficient human 3D localization and free space segmentation for human-aware mobile robots in warehouse facilities

Obstacle detection method based on point cloud registration and target reduction processing efficient detection of vehicle obstacles

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