Detect-Slam: Making Object Detection And Slam Mutually Beneficial
2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018)(2018)
摘要
Although significant progress has been made in SLAM and object detection in recent years, there are still a series of challenges for both tasks, e.g., SLAM in dynamic environments and detecting objects in complex environments. To address these challenges, we present a novel robotic vision system, which integrates SLAM with a deep neural network-based object detector to make the two functions mutually beneficial. The proposed system facilitates a robot to accomplish tasks reliably and efficiently in an unknown and dynamic environment. Experimental results show that compare to the state-of-the-art robotic vision systems, the proposed system has three advantages: i) it greatly improves the accuracy and robustness of SLAM in dynamic environments by removing unreliable features from moving objects leveraging the object detector, ii) it builds an instance-level semantic map of the environment in an online fashion using the synergy of the two functions for further semantic applications; and iii) it improves the object detector so that it can detect/recognize objects effectively under more challenging conditions such as unusual viewpoints, poor lighting condition, and motion blur, by leveraging the object map.
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关键词
detect-SLAM,object detection,SLAM mutually beneficial,dynamic environments,complex environments,robotic vision system,deep neural network,instance-level semantic map,unusual viewpoints,poor lighting condition,motion blur,object map leveraging
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