Robustness and safety are crucial properties for the real-world application of autonomous vehicles. One of the most critical components of any autonomous system is localisation. During the last 20 years there has been significant progress in this area with the introduction of very efficient algorithms for mapping, localisation and SLAM. Many of these algorithms present impressive demonstrations for a particular domain, but fail to operate reliably with changes to the operating environment. The aspect of robustness has not received enough attention and localisation systems for self-driving vehicle applications are seldom evaluated for their robustness. In this paper we propose novel metrics to effectively quantify localisation robustness with or without an accurate ground truth. The experimental results present a comprehensive analysis of the application of these metrics against a number of well known localisation strategies.
There is a considerable body of research focusing on improving visual localisation robustness due to the brittle nature of visual features with time of the day, lighting, or other environmental variables. One example of this research is @cite_9 . In this work, they design a ranking function and assign a quality rank for each map feature so that an adaptive feature selection policy can be enacted according to variation in the appearance of features due to events such as time of day or night. @cite_4 tested robustness against fast motion and undesirable lightening conditions. They concluded robustness and accuracy is a trade-off when using fish-eye cameras, and improved robustness by incorporating multi-sensor fusion. @cite_7 proposed geometric point quality, point recognition probability and localisation quality as robustness metrics, and presented methodology specific to visual features and localisation.
@cite_9 @cite_4 @cite_7
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This paper is about camera-only localisation in challenging outdoor environments, where changes in lighting, weather and season cause traditional localisation systems to fail. Conventional approaches to the localisation problem rely on point-features such as SIFT, SURF or BRIEF to associate landmark observations in the live image with landmarks stored in the map; however, these features are brittle to the severe appearance change routinely encountered in outdoor environments. In this paper, we propose an alternative to traditional point-features: we train place-specific linear SVM classifiers to recognise distinctive elements in the environment. The core contribution of this paper is an unsupervised mining algorithm which operates on a single mapping dataset to extract distinct elements from the environment for localisation. We evaluate our system on 205km of data collected from central Oxford over a period of six months in bright sun, night, rain, snow and at all times of the day. Our experiment consists of a comprehensive N-vs-N analysis on 22 laps of the approximately 10km route in central Oxford. With our proposed system, the portion of the route where localisation fails is reduced by a factor of 6, from 33.3 to 5.5 . We present a method of improving visual place recognition and metric localisation under very strong appear- ance change. We learn an invertable generator that can trans- form the conditions of images, e.g. from day to night, summer to winter etc. This image transforming filter is explicitly designed to aid and abet feature-matching using a new loss based on SURF detector and dense descriptor maps. A network is trained to output synthetic images optimised for feature matching given only an input RGB image, and these generated images are used to localize the robot against a previously built map using traditional sparse matching approaches. We benchmark our results using multiple traversals of the Oxford RobotCar Dataset over a year-long period, using one traversal as a map and the other to localise. We show that this method significantly improves place recognition and localisation under changing and adverse conditions, while reducing the number of mapping runs needed to successfully achieve reliable localisation. Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100 precision with recall rates of up to 60 . Most visual simultaneous localization and mapping systems use point features as their landmarks and adopt point-based feature descriptors to recognize them. Compared to point landmarks, however, lines have strength in conveying the structural information of the environment. Despite the benefit, they have not been widely used because lines are more difficult in detecting, tracking, and recognizing, and this delayed the use of lines as landmarks. In this paper, we propose a place recognition algorithm using straight line features, which enables reliable loop closure detections in large complex environments under significant illumination changes. A vocabulary tree trained with mean standard-deviation line descriptor is used in finding the candidate matches between keyframes, and a Bayesian filtering framework enables reliable keyframe matching for large-scale loop closures. The proposed algorithm is compared with state-of-the-art point-based methods using scale-invariant feature transform or speeded up robust features. The experimental results show that the proposed method outperforms the others in challenging indoor environments.