13 accepted papers
CNNs have excelled at performing place recognition over time, particularly when the neural network is optimized for localization in the current environmental conditions. In this paper we investigate the concept of feature map filtering, where, rather than using all the activations within a convoluti…
A key capability for autonomous underground mining vehicles is real-time accurate localisation. While significant progress has been made, currently deployed systems have several limitations ranging from dependence on costly additional infrastructure to failure of both visual and range-sensor-based t…
Typical attempts to improve the capability of visual place recognition techniques include the use of multi-sensor fusion and the integration of information over time from image sequences. These approaches can improve performance but have disadvantages, including the need for multiple physical sensor
Many mine-site processes such as vehicle operation require localisation systems that are reliable, robust and work in a range of environmental conditions. In underground operations, GPS is not available: solutions instead rely on static infrastructure or expensive, laser-based solutions with limited
Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With
This work presents Semi-Supervised SLAM - a method for developing a map suitable for coarse localization within an underground environment with minimal human intervention, with system characteristics driven by real-world requirements of major mining companies. This work leverages existing informatio…
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks. In this paper, we train, at large sc…
Learning and recognition is a fundamental process performed in many robot operations such as mapping and localization. The majority of approaches share some common characteristics, such as attempting to extract salient features, landmarks or signatures, and growth in data storage and computational r…
The place recognition problem comprises two distinct subproblems; recognizing a specific location in the world (“specific” or “ordinary” place recognition) and recognizing the type of place (place categorization). Both are important competencies for mobile robots and have each received significant a…
Many localization algorithms use a spatiotemporal window of sensory information in order to recognize spatial locations, and the length of this window is often a sensitive parameter that must be tuned to the specifics of the application. This letter presents a general method for environment-driven v
The recent focus on performing visual navigation and place recognition in changing environments has resulted in a large number of heterogeneous techniques each utilizing their own learnt or hand crafted visual features. This paper presents a generally applicable method for learning the appropriate d…
Localization is a critical capability for many autonomous mobile robots. Implicit in all localization and place recognition techniques is the ability to decide whether a place is novel or familiar; the idea of “has this place been visited before?”. To make this decision accurately, localization syst…
Place recognition has long been an incompletely solved problem in that all approaches involve significant com- promises. Current methods address many but never all of the critical challenges of place recognition _ viewpoint-invariance, condition-invariance and minimizing training requirements. Here…