Understanding how camera configuration and environmental conditions affect appearance-based localization

Bansal, Aayush; Badino, Hernán; Huber, Daniel

doi:10.1109/ivs.2014.6856605

Cited by 23 publications

(13 citation statements)

References 23 publications

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“…The database image sequences and evaluation image sequences were collected at different times on a single day. More extensive analyses the effects of illumination and appearance variance were presented in [4], [5], and [6]. Ideally, our experiments would use an established database, such as the KITTI benchmark [13].…”

Section: Methodsmentioning

confidence: 99%

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Topometric localization on a road network

Badino

Huber

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Current GPS-based devices have difficulty localizing in cases where the GPS signal is unavailable or insufficiently accurate. This paper presents an algorithm for localizing a vehicle on an arbitrary road network using vision, road curvature estimates, or a combination of both. The method uses an extension of topometric localization, which is a hybrid between topological and metric localization. The extension enables localization on a network of roads rather than just a single, non-branching route. The algorithm, which does not rely on GPS, is able to localize reliably in situations where GPSbased devices fail, including "urban canyons" in downtown areas and along ambiguous routes with parallel roads. We demonstrate the algorithm experimentally on several road networks in urban, suburban, and highway scenarios. We also evaluate the road curvature descriptor and show that it is effective when imagery is sparsely available.

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Section: Methodsmentioning

confidence: 99%

“…However, this is not such an onerous requirement, since such maps already exist (e.g., Google Street View). Our previous work has shown that the maps do not have to be updated very frequently, as localization is robust to changing conditions, such as seasonal variations and different weather and light conditions [5], [6].…”

Section: Related Workmentioning

confidence: 99%

Topometric localization on a road network

Badino

Huber

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…As mentioned in [26], vision-based algorithms may have very different performance with different camera configurations and environmental conditions. This aspect of reliability and integrity is of significant importance for autonomous vehicles.…”

Section: Segmentation Reliabilitymentioning

confidence: 99%

Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving

Zhou

Berrío

Worrall

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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One of the fundamental challenges in the design of perception systems for autonomous vehicles is validating the performance of each algorithm under a comprehensive variety of operating conditions. In the case of vision-based semantic segmentation, there are known issues when encountering new scenarios that are sufficiently different to the training data. In addition, even small variations in environmental conditions such as illumination and precipitation can affect the classification performance of the segmentation model. Given the reliance on visual information, these effects often translate into poor semantic pixel classification which can potentially lead to catastrophic consequences when driving autonomously. This paper presents a novel method for analysing the robustness of semantic segmentation models and provides a number of metrics to evaluate the classification performance over a variety of environmental conditions. The process incorporates an additional sensor (lidar) to automate the process, eliminating the need for labour-intensive hand labelling of validation data. The system integrity can be monitored as the performance of the vision sensors are validated against a different sensor modality. This is necessary for detecting failures that are inherent to vision technology. Experimental results are presented based on multiple datasets collected at different times of the year with different environmental conditions. These results show that the semantic segmentation performance varies depending on the weather, camera parameters, existence of shadows, etc.. The results also demonstrate how the metrics can be used to compare and validate the performance after making improvements to a model, and compare the performance of different networks.

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“…SLAM has long been a classic topic in the field of robotics [14] and has become a popular research direction in autonomous driving since many drivable areas are GNSS denied, such as indoor parking lots and urban roads covered by tree branches or beside skyscrapers [15]. VSLAM, which is based on budget vision sensors, is preferred by many car manufacturers who have already equipped multiple cameras on their production cars.…”

Section: Related Workmentioning

confidence: 99%

Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

Zhao

Huang

et al. 2019

Sensors

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Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.

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Understanding how camera configuration and environmental conditions affect appearance-based localization

Cited by 23 publications

References 23 publications

Topometric localization on a road network

Topometric localization on a road network

Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving

Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

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