Luke Fletcher

Real-Time Speed Sign Detection Using the Radial Symmetry Detector

Algorithms for classifying road signs have a high computational cost per pixel processed. A detection stage that has a lower computational cost can facilitate real-time processing. Various authors have used shape and color-based detectors. Shape-based detectors have an advantage under variable lighting conditions and sign deterioration that, although the apparent color may change, the shape is preserved. In this paper, we present the radial symmetry detector for detecting speed signs. We evaluate the detector itself in a system that is mounted within a road vehicle. We also evaluate its performance that is integrated with classification over a series of sequences from roads around Canberra and demonstrate it while running online in our road vehicle. We show that it can detect signs with high reliability in real time. We examine the internal parameters of the algorithm to adapt it to road sign detection. We demonstrate the stability of the system under the variation of these parameters and show computational speed gains through their tuning. The detector is demonstrated to work under a wide variety of visual conditions.

Multiple relative pose graphs for robust cooperative mapping

This paper describes a new algorithm for cooperative and persistent simultaneous localization and mapping (SLAM) using multiple robots. Recent pose graph representations have proven very successful for single robot mapping and localization. Among these methods, incremental smoothing and mapping (iSAM) gives an exact incremental solution to the SLAM problem by solving a full nonlinear optimization problem in real-time. In this paper, we present a novel extension to iSAM to facilitate online multi-robot mapping based on multiple pose graphs. Our main contribution is a relative formulation of the relationship between multiple pose graphs that avoids the initialization problem and leads to an efficient solution when compared to a completely global formulation. The relative pose graphs are optimized together to provide a globally consistent multi-robot solution. Efficient access to covariances at any time for relative parameters is provided through iSAM, facilitating data association and loop closing. The performance of the technique is illustrated on various data sets including a publicly available multi-robot data set. Further evaluation is performed in a collaborative helicopter and ground robot experiment.

Driver Inattention Detection based on Eye Gaze-Road Event Correlation

Current road safety initiatives are approaching the limit of their effectiveness in developed countries. A paradigm shift is needed to address the preventable deaths of thousands on our roads. Previous systems have focused on one or two aspects of driving: environmental sensing, vehicle dynamics or driver monitoring. Our approach is to consider the driver and the vehicle as part of a combined system, operating within the road environment. A driver assistance system is implemented that is not only responsive to the road environment and the drivers actions but also designed to correlate the drivers eye gaze with road events to determine the drivers observations. Driver observation monitoring enables an immediate in-vehicle system able to detect and act on driver inattentiveness, providing the precious seconds for an inattentive human driver to react. We present a prototype system capable of estimating the drivers observations and detecting driver inattentiveness. Due to the “look but not see” case it is not possible to prove that a road event has been observed by the driver. We show, however, that it is possible to detect missed road events and warn the driver appropriately.

Correlating driver gaze with the road scene for driver assistance systems

A driver assistance system (DAS) should support the driver by monitoring road and vehicle events and presenting relevant and timely information to the driver. It is impossible to know what a driver is thinking, but we can monitor the drivers gaze direction and compare it with the position of information in the drivers viewfield to make inferences. In this way, not only do we monitor the drivers actions, we monitor the drivers observations as well. In this paper we present the automated detection and recognition of road signs, combined with the monitoring of the drivers response. We present a complete system that reads speed signs in real-time, compares the drivers gaze, and provides immediate feedback if it appears the sign has been missed by the driver.

An adaptive fusion architecture for target tracking

A vision system is demonstrated that adaptively allocates computational resources over multiple cues to robustly track a target in 3D. The system uses a particle filter to maintain multiple hypotheses of the target location. Bayesian probability theory provides the framework for sensor fusion, and resource scheduling is used to intelligently allocate the limited computational resources available across the suite of cues. The system is shown to track a person in 3D space moving in a cluttered environment.

Vision in and out of vehicles

At the Australian National Universitys Intelligent Vehicle Project, we are developing subsystems for: driver fatigue or inattention detection; pedestrian spotting; blind-spot checking and merging assistance to validate whether sufficient clearance exists between cars; driver feedback for lane keeping; computer-augmented vision (that is, lane boundary or vehicle highlighting on a head-up display); traffic sign detection and recognition; and human factors research aids Systems that perform such supporting tasks are generally called driver assistance systems (DAS). We believe that implementing DAS could prevent similar accidents or at least reduce their severity.

A voice-commandable robotic forklift working alongside humans in minimally-prepared outdoor environments

One long-standing challenge in robotics is the realization of mobile autonomous robots able to operate safely in existing human workplaces in a way that their presence is accepted by the human occupants. We describe the development of a multi-ton robotic forklift intended to operate alongside human personnel, handling palletized materials within existing, busy, semi-structured outdoor storage facilities.

Driver assistance systems based on vision in and out of vehicles

As computer vision based systems like lane tracking, face tracking and obstacle detection mature an enhanced range of driver assistance systems are becoming feasible. This paper introduces a list of core competencies required for a driver assistance system, the issue of building in robustness is highlighted in contrast to leaving such considerations to a later product development phase. We then demonstrate how these issues may be addressed in driver assistance systems based primarily on computer vision. The underlying computer vision systems are discussed followed by an example of a driver support application for lane keeping based on force-feedback through the steering wheel.

Road scene monotony detection in a fatigue management driver assistance system

Automated fatigue detection devices show much promise in combating fatigue related accidents. One aspect which hampers the introduction of these technologies is context awareness. In this paper we develop and evaluate a road scene monotony detector. The detector can be used to give context awareness to fatigue detection tools to minimise false positives. The approach could also be used by road makers to quantify monotony on fatigue prone stretches of road. The detector uses MPEG compression to measure the change in information content of the road scene over time. We show that the detector correlates highly with human identified monotonous scenes. The technique is consistent over time and applicable for day and night operation. The compression is augmented with lane tracking data to distinguish between otherwise difficult cases. The detector is integrated into a fatigue management driver assistance system.

A High-rate, Heterogeneous Data Set From The DARPA Urban Challenge

This paper describes a data set collected by MIT’s autonomous vehicle Talos during the 2007 DARPA Urban Challenge. Data from a high-precision navigation system, five cameras, 12 SICK planar laser range scanners, and a Velodyne high-density laser range scanner were synchronized and logged to disk for 90 km of travel. In addition to documenting a number of large loop closures useful for developing mapping and localization algorithms, this data set also records the first robotic traffic jam and two autonomous vehicle collisions. It is our hope that this data set will be useful to the autonomous vehicle community, especially those developing robotic perception capabilities.