Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization

• URL: http://arxiv.org/abs/2307.01121v2
• Date: Tue, 21 Nov 2023 21:04:24 GMT
• Title: Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization
• Authors: Federico Rollo, Gennaro Raiola, Andrea Zunino, Nikolaos Tsagarakis, Arash Ajoudani
• Abstract summary: We propose a framework that can autonomously detect and localize objects in a known environment. The framework consists of three key elements: understanding the environment through RGB data, estimating depth through multi-modal sensor fusion, and managing artifacts. Experiments show that the proposed framework can accurately detect 98% of the objects in the real sample environment, without post-processing.
• Score: 13.473742114288616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geometric navigation is nowadays a well-established field of robotics and the research focus is shifting towards higher-level scene understanding, such as Semantic Mapping. When a robot needs to interact with its environment, it must be able to comprehend the contextual information of its surroundings. This work focuses on classifying and localising objects within a map, which is under construction (SLAM) or already built. To further explore this direction, we propose a framework that can autonomously detect and localize predefined objects in a known environment using a multi-modal sensor fusion approach (combining RGB and depth data from an RGB-D camera and a lidar). The framework consists of three key elements: understanding the environment through RGB data, estimating depth through multi-modal sensor fusion, and managing artifacts (i.e., filtering and stabilizing measurements). The experiments show that the proposed framework can accurately detect 98% of the objects in the real sample environment, without post-processing, while 85% and 80% of the objects were mapped using the single RGBD camera or RGB + lidar setup respectively. The comparison with single-sensor (camera or lidar) experiments is performed to show that sensor fusion allows the robot to accurately detect near and far obstacles, which would have been noisy or imprecise in a purely visual or laser-based approach.

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