Convolutional Neural Network-based Place Recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.Comment: 8 pages, 11 figures, this paper has been accepted by 2014 Australasian Conference on Robotics and Automation (ACRA 2014) to be held in University of Melbourne, Dec 2~

Automated topometric graph generation from floor plan analysis

The world is rich with information such as signage and maps to assist humans to navigate. We present a method to extract topological spatial information from a generic bitmap floor plan and build a topometric graph that can be used by a mobile robot for tasks such as path planning and guided exploration. The algorithm first detects and extracts text in an image of the floor plan. Using the locations of the extracted text, flood fill is used to find the rooms and hallways. Doors are found by matching SURF features and these form the connections between rooms, which are the edges of the topological graph. Our system is able to automatically detect doors and differentiate between hallways and rooms, which is important for effective navigation. We show that our method can extract a topometric graph from a floor plan and is robust against ambiguous cases most commonly seen in floor plans including elevators and stairwells

Text recognition approaches for indoor robotics: a comparison

This paper evaluates the performance of different text recognition techniques for a mobile robot in an indoor (university campus) environment. We compared four different methods: our own approach using existing text detection methods (Minimally Stable Extremal Regions detector and Stroke Width Transform) combined with a convolutional neural network, two modes of the open source program Tesseract, and the experimental mobile app Google Goggles. The results show that a convolutional neural network combined with the Stroke Width Transform gives the best performance in correctly matched text on images with single characters whereas Google Goggles gives the best performance on images with multiple words. The dataset used for this work is released as well

Spectra: 3D multispectral fusion and visualization toolkit

There is an increasing demand for accurate and intuitive representation of both thermographic and multispectral data in 3-Dimensional models. Existing fusion methods can be used to compensate for the typically low spatial resolution of multispectral images with high spatial resolution visiblespectrum images, overlaying the combined result on a 3D model. However, because of a lack of appropriate methods, thermal information has in the past been displayed on a separate model in a separate screen, which increases the cognitive workload of the user. To address this, we present a suite of novel mesh-based fusion methods that can represent both thermal and visible-spectrum data simultaneously on a single 3D model, highlighting regions of extreme temperature or informative temperature variation. These methods are targeted towards different applications and as a result have unique advantages. In addition, a 3D multispectral visualization toolkit "Spectra" has been developed. This visualizer allows the user to customize settings for each fusion method, including scaling and thresholding factors and many method-specific parameters. The "Spectra" toolkit is used to efficiently assess the effectiveness of the fusion methods for a variety of tasks

Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration

In this paper we present for the first time a complete symbolic navigation system that performs goal-directed exploration to unfamiliar environments on a physical robot. We introduce a novel construct called the abstract map to link provided symbolic spatial information with observed symbolic information and actual places in the real world. Symbolic information is observed using a text recognition system that has been developed specifically for the application of reading door labels. In the study described in this paper, the robot was provided with a floor plan and a destination. The destination was specified by a room number, used both in the floor plan and on the door to the room. The robot autonomously navigated to the destination using its text recognition, abstract map, mapping, and path planning systems. The robot used the symbolic navigation system to determine an efficient path to the destination, and reached the goal in two different real-world environments. Simulation results show that the system reduces the time required to navigate to a goal when compared to random exploration

Find my office: Navigating real space from semantic descriptions

This paper shows that by using only symbolic language phrases, a mobile robot can purposefully navigate to specified rooms in previously unexplored environments. The robot intelligently organises a symbolic language description of the unseen environment and “imagines” a representative map, called the abstract map. The abstract map is an internal representation of the topological structure and spatial layout of symbolically defined locations. To perform goal-directed exploration, the abstract map creates a high-level semantic plan to reason about spaces beyond the robot’s known world. While completing the plan, the robot uses the metric guidance provided by a spatial layout, and grounded observations of door labels, to efficiently guide its navigation. The system is shown to complete exploration in unexplored spaces by travelling only 13.3% further than the optimal path

Technique to produce catalyst from egg shell and coconut waste for biodiesel production

Many studies have been conducted to develop low cost catalysts to reduce the production cost. Several catalysts such as homogeneous/heterogeneous acid catalysts, homogeneous/heterogeneous base catalysts and biocatalysts (enzymes) have been studied and applied in the synthesis of biodiesel. Base- catalyzed transesterification is commonly used in commercial production because of high FAME yield in short reaction time and the reaction can be done in mild conditions as compared to acid-catalyzed transesterification. In the present study, egg shell and coconut waste were synthesized using calcination method at 800 °C for 4 h. SEM micrographs prove that the mixture of catalyst shows a bigger surface area. This result is expected to increase the yield of biodiesel. It can be concluded that biodiesel was produced successfully using palm oil and mixed catalysts