Evaluation of Long-term LiDAR Place Recognition

We compare a state-of-the-art deep image retrieval and a deep place recognition method for place recognition using LiDAR data. Place recognition aims to detect previously visited locations and thus provides an important tool for navigation, mapping, and localisation. Experimental comparisons are conducted using challenging outdoor and indoor datasets, Oxford Radar RobotCar and COLD, in the "long-term" setting where the test conditions differ substantially from the training and gallery data. Based on our results the image retrieval methods using LiDAR depth images can achieve accurate localization (the single best match recall 80%) within 5.00 m in urban outdoors. In office indoors the comparable accuracy is 50 cm but is more sensitive to changes in the environment.acceptedVersionPeer reviewe

Double-domain Adaptation Semantics for Retrieval-based Long-term Visual Localization

Due to seasonal and illumination variance, long-term visual localization tasks in dynamic environments is a crucial problem in the field of autonomous driving and robotics. At present, image-based retrieval is an effective method to solve this problem. However, it is difficult to completely distinguish changes in the same location over times by relying on content information alone. In order to solve these above problems, a double-domain network model combining semantic information and content information is proposed for visual localization task. In addition, this approach only needs to use the virtual KITTI 2 dataset for training. To reduce the domain difference between real scene and virtual image, the cross-predictive semantic segmentation mechanism is introduced to solve this problem. In addition, the obtained model achieves good domain adaptation and further has well generalization on other real datasets by introducing a domain loss function and a triplet semantic loss function. A series of experiments on the Extended CMU-Seasons dataset and the Oxford RobotCar-Seasons dataset demonstrates that the proposed network model outperformes the state-of-the-art baselines for retrieval-based visual localization in challenging environments

Long-Term Visual Localization Revisited

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas. Our datasets are available at visuallocalization.net, where we are also hosting a benchmarking server for automatic evaluation of results on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server

LiDAR Place Recognition with Image Retrieval

This thesis is about LiDAR place recognition. Place recognition is the problem of being able to recognize already seen or visited places – an important sub-problem of robot navigation. LiDAR sensors offer accurate and cost-effective range and reflec-tivity data that can replace or complement RGB cameras. Place recognition has been studied with different sensors and methods for many years. Traditional methods use handcrafted features to match images in order to recognize places. In recent years, the surge of deep learning has made learned features the main approach. In this work LiDAR place recognition is studied with exported 2D pixel images and deep learning models. Place recognition is posed as an image retrieval problem, where a model is trained to learn a feature space in such a way that the similarity of images can be conveniently compared. With a trained model, one can use an image to search for other similar images, and thus recognize places. The key finding of the thesis publications is that place recognition with image retrieval using exported pixel images from LiDAR scans is a well performing method, as evidenced by achieving about 80% recall@1 with 5 meter test distance in urban outdoors and 1 meter indoors. The other key findings are: Loop points in the route are detectable with image retrieval type methods. LiDAR is a competitive modality versus RGB. LiDAR depth maps are more robust to change than intensity maps. Generalized mean is a good pooling method for place recognition. Simulated data is beneficial when mixed in with real-world data at a suitable ratio. Dataset quality is very important in regards to ground truth position and LiDAR resolution