Abstract
Abstract. Place recognition or loop closure is a technique to recognize landmarks and/or scenes visited by a mobile sensing platform previously in an area. The technique is a key function for robustly practicing Simultaneous Localization and Mapping (SLAM) in any environment, including the global positioning system (GPS) denied environment by enabling to perform the global optimization to compensate the drift of dead-reckoning navigation systems. Place recognition in 3D point clouds is a challenging task which is traditionally handled with the aid of other sensors, such as camera and GPS. Unfortunately, visual place recognition techniques may be impacted by changes in illumination and texture, and GPS may perform poorly in urban areas. To mitigate this problem, state-of-art Convolutional Neural Networks (CNNs)-based 3D descriptors may be directly applied to 3D point clouds. In this work, we investigated the performance of different classification strategies utilizing a cutting-edge CNN-based 3D global descriptor (PointNetVLAD) for place recognition task on the Oxford RobotCar dataset.
Highlights
One important aspect of Simultaneous Localization and Mapping (SLAM) algorithms is that the localization errors keep accumulating as the number of measurements keeps increasing, due to the errors in measurements caused by the noise of sensors (Dhiman et al, 2015)
SLAM algorithms rely on place recognition (PR), or loop closure detection (LCD) techniques, wherein the algorithms are able to recognize previously visited places and use them as additional constraints for increasing the precision of localization estimation and solving the global localization problem
The major limitation for extracting semantic features is the assumption that there are enough static objects which have been adequately learned by the pretrained Convolutional Neural Networks (CNNs) model
Summary
One important aspect of SLAM algorithms is that the localization errors keep accumulating as the number of measurements keeps increasing, due to the errors in measurements caused by the noise of sensors (Dhiman et al, 2015). The major limitation for extracting semantic features is the assumption that there are enough static objects which have been adequately learned by the pretrained CNN model This assumption may not always be satisfied in real-world practice. One interesting task in the real-world PR practice is classification under the restriction that we may only observe a single example of each possible scenario before making a prediction about a test instance This problem is known as one-shot learning (Koch et al, 2015), and the Siamese neural networks have been demonstrated as an effective solution for one-shot learning in imagery application (Yin W et al, 2015) and low dimensional 3D semantic segment descriptors classification (Cramariuc et al, 2018).
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