Hugh Durrant-Whyte

Autonomous vehicle operations in outdoor environments challenge robotic perception. Construction, mining, agriculture, and planetary exploration environments are examples in which the presence of dust, fog, rain, changing illumination due to low sun angles, and lack of contrast can dramatically degrade conventional stereo and laser sensing. Nonetheless, environment perception can still succeed under compromised visibility through the use of a millimeter‐wave radar. Radar also allows for multiple object detection within a single beam, whereas other range sensors are limited to one target return per emission. However, radar has shortcomings as well, such as a large footprint, specularity effects, and limited range resolution, all of which may result in poor environment survey or difficulty in interpretation. This paper presents a novel method for ground segmentation using a millimeter‐wave radar mounted on a ground vehicle. Issues relevant to short‐range perception in an outdoor environment are described along with field experiments and a quantitative comparison to laser data. The ability to classify the ground is successfully demonstrated in clear and low‐visibility conditions, and significant improvement in range accuracy is shown. Finally, conclusions are drawn on the utility of millimeter‐wave radar as a robotic sensor for persistent and accurate perception in natural scenarios.

Building a model of large‐scale terrain that can adequately handle uncertainty and incompleteness in a statistically sound way is a challenging problem. This work proposes the use of Gaussian processes as models of large‐scale terrain. The proposed model naturally provides a multiresolution representation of space, incorporates and handles uncertainties aptly, and copes with incompleteness of sensory information. Gaussian process regression techniques are applied to estimate and interpolate (to fill gaps in occluded areas) elevation information across the field. The estimates obtained are the best linear unbiased estimates for the data under consideration. A single nonstationary (neural network) Gaussian process is shown to be powerful enough to model large and complex terrain, effectively handling issues relating to discontinuous data. A local approximation method based on a “moving window” methodology and implemented using k‐dimensional (KD)‐trees is also proposed. This enables the approach to handle extremely large data sets, thereby completely addressing its scalability issues. Experiments are performed on large‐scale data sets taken from real mining applications. These data sets include sparse mine planning data, which are representative of a global positioning system–based survey, as well as dense laser scanner data taken at different mine sites. Further, extensive statistical performance evaluation and benchmarking of the technique has been performed through cross‐validation experiments. They conclude that for dense and/or flat data, the proposed approach will perform very competitively with grid‐based approaches using standard interpolation techniques and triangulated irregular networks using triangle‐based interpolation techniques; for sparse and/or complex data, however, it would significantly outperform them.

Simultaneous localization, mapping and moving object tracking (SLAMMOT) involves both simultaneous localization and mapping (SLAM) in dynamic environments and detecting and tracking these dynamic objects. In this paper, a mathematical framework is established to integrate SLAM and moving object tracking. Two solutions are described: SLAM with generalized objects, and SLAM with detection and tracking of moving objects (DATMO). SLAM with generalized objects calculates a joint posterior over all generalized objects and the robot. Such an approach is similar to existing SLAM algorithms, but with additional structure to allow for motion modeling of generalized objects. Unfortunately, it is computationally demanding and generally infeasible. SLAM with DATMO decomposes the estimation problem into two separate estimators. By maintaining separate posteriors for stationary objects and moving objects, the resulting estimation problems are much lower dimensional than SLAM with generalized objects. Both SLAM and moving object tracking from a moving vehicle in crowded urban areas are daunting tasks. Based on the SLAM with DATMO framework, practical algorithms are proposed which deal with issues of perception modeling, data association, and moving object detection. The implementation of SLAM with DATMO was demonstrated using data collected from the CMU Navlab11 vehicle at high speeds in crowded urban environments. Ample experimental results shows the feasibility of the proposed theory and algorithms.

In this paper, simultaneous localisation and mapping (SLAM) is combined with landmark recognition to close large loops in unstructured, outdoor environments. Camera and laser information are fused to recognise and create appearance models for landmarks. The representation is obtained through a non-linear probabilistic regression model encoding a neighbourhood preserving dimensionality reduction. A new data association algorithm is proposed where landmarks are associated based on both position and appearance. The resulting system is more robust and able to recover from possible misassociations. Experiments demonstrate the benefits of this approach in challenging problems involving mapping with large loop closings in irregular terrain, and with dynamic objects.

This paper examines the use of millimeter‐wave radar systems for visualization and navigation in unstructured outdoor environments. Three types of radar systems are described. The first is a long range, 94 GHz, frequency modulated interrupted continuous wave radar which is capable of producing two‐dimensional (2D) reflectivity images to a range of more than 3 km. This is intended for use in long‐range path planning. The second is a class of medium range 77 GHz frequency modulated continuous wave (FMCW) radar with two axis mirror scanners which is capable of producing high resolution threedimensional (3D) imagery out to 500 m at a reasonably slow frame rate. The final class also operates using the FMCW principle, but at 94 GHz, to produce high resolution 2D and 3D images out to about 50 m at a much higher update rate. These shorter range sensors may be used to determine the traversability of the local terrain. The outputs produced by the different classes of radar are examined and the paper considers their advantages when compared to other sensors such as vision and scanning laser. Using radar images, the final section compiles rules for interpreting radar reflectivity images from a path‐planning perspective.

This paper presents an algorithm for recognition and segmentation of natural features in unstructured environments. By providing a Bayesian solution for the density estimation problem, the algorithm needs significantly less training data than conventional techniques and is applicable to different environments. The algorithm is based on colour and wavelet convolution of image patches to model the information contained in natural features. Dimensionality reduction techniques are applied to map data points to a lower dimensional space where Bayesian density estimation is computed. Experiments were performed in underwater, aerial and terrestrial domains demonstrating the accuracy and generalisation properties of the algorithm for recognition and segmentation. Comparisons with conventional density estimation techniques are provided to illustrate the benefits of the new approach