The organization of behavior: A neuropsychological theory

IEEE Transactions on Pattern Analysis and Machine Intelligence

https://www.bmj.com/content/bmj/1/5125/836.2.full.pdf

Dictionary of psychology

In this paper, we present a novel mapping system that robustly generates highly accurate 3-D maps using an RGB-D camera. Our approach requires no further sensors or odometry. With the availability of low-cost and light-weight RGB-D sensors such as the Microsoft Kinect, our approach applies to small domestic robots such as vacuum cleaners, as well as flying robots such as quadrocopters. Furthermore, our system can also be used for free-hand reconstruction of detailed 3-D models. In addition to the system itself, we present a thorough experimental evaluation on a publicly available benchmark dataset. We analyze and discuss the influence of several parameters such as the choice of the feature descriptor, the number of visual features, and validation methods. The results of the experiments demonstrate that our system can robustly deal with challenging scenarios such as fast camera motions and feature-poor environments while being fast enough for online operation. Our system is fully available as open source and has already been widely adopted by the robotics community.

3-D Mapping With an RGB-D Camera

We present a new simultaneous localization and mapping SLAM system capable of producing high-quality globally consistent surface reconstructions over hundreds of meters in real time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every-frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an 'as-rigid-as-possible' space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.

/pdf/real-time-large-scale-dense-rgb-d-slam-with-volumetric-nsvs7jcopb.pdf

Real-time large-scale dense RGB-D SLAM with volumetric fusion

Recently RGB-D sensors have become very popular in the area of Simultaneous Localisation and Mapping (SLAM). The major advantage of these sensors is that they provide a rich source of 3D information at relatively low cost. Unfortunately, these sensors in their current forms only have a range accuracy of up to 4 metres. Many techniques which perform SLAM using RGB-D cameras rely heavily on the depth and are restrained to office type and geometrically structured environments. In this paper, a switching based algorithm is proposed to heuristically choose between RGB-BA and RGBD-BA based local maps building. Furthermore, a low cost and consistent optimisation approach is used to join these maps. Thus the potential of both RGB and depth image information are exploited to perform robust SLAM in more general indoor cases. Validation of the proposed algorithm is performed by mapping a large scale indoor scene where traditional RGB-D mapping techniques are not possible.

A robust RGB-D SLAM algorithm

Most of the lane marking detection algorithms reported in the literature are suitable for highway scenarios. This paper presents a novel clustered particle filter based approach to lane detection, which is suitable for urban streets in normal traffic conditions. Furthermore, a quality measure for the detection is calculated as a measure of reliability. The core of this approach is the usage of weak models, i.e. the avoidance of strong assumptions about the road geometry. Experiments were carried out in Sydney urban areas with a vehicle mounted laser range scanner and a ccd camera. Through experimentations, we have shown that a clustered particle filter can be used to efficiently extract lane markings.

/pdf/robust-lane-detection-in-urban-environments-2z40el5e45.pdf

Robust lane detection in urban environments

Observing human motion patterns is informative for social robots that share the environment with people. This paper presents a methodology to allow a robot to navigate in a complex environment by observing pedestrian positional traces. A continuous probabilistic function is determined using Gaussian process learning and used to infer the direction a robot should take in different parts of the environment. The approach learns and filters noise in the data producing a smooth underlying function that yields more natural movements. Our method combines prior conventional planning strategies with most probable trajectories followed by people in a principled statistical manner, and adapts itself online as more observations become available. The use of learning methods are automatic and require minimal tuning as compared to potential fields or spline function regression. This approach is demonstrated testing in cluttered office and open forum environments using laser and vision sensing modalities. It yields paths that are similar to the expected human behaviour without any a priori knowledge of the environment or explicit programming.

/pdf/learning-navigational-maps-by-observing-human-motion-2c3ailwbrs.pdf

Learning navigational maps by observing human motion patterns

Lane detection in urban environments is a challenging task That is mainly due to the non existence of unique models, poor quality of lane markings due to wear, occlusions due to the presence of traffic and complex road geometry In this work we present a novel lane detection and tracking algorithm for urban road scenarios based on weak models, which is implemented by a particle filter The algorithm is implemented and experiments were carried out on Sydney urban roads The results show the robustness of the algorithm to the problems inherent in urban road environments

/pdf/efficient-lane-detection-and-tracking-in-urban-environments-48zncgluw3.pdf

Efficient Lane Detection and Tracking in Urban Environments.

A ferromagnetic material thickness quantification method based on the decay rate of the Pulsed Eddy Current sensor detector coil voltage is proposed. An expression for the decay rate is derived and the relationship between the decay rate and material thickness is established. Pipe wall thickness estimation is done with a developed circular sensor incorporating the proposed method, and results are evaluated through destructive testing. The decay rate feature has a unique attribute of being lowly dependent on properties such as sensor shape and size, and lift-off, enabling the method to be usable with any detector coil-based sensor. A case study on using the proposed method with a commercial sensor is also presented to demonstrate its versatility.

Alen Alempijevic

Papers

A robust RGB-D SLAM algorithm

Robust lane detection in urban environments

Learning navigational maps by observing human motion patterns

Efficient Lane Detection and Tracking in Urban Environments.

Non-destructive evaluation of ferromagnetic material thickness using Pulsed Eddy Current sensor detector coil voltage decay rate