Exploring the metaphorical terrain

Joanne Lee participated as one of four ‘Seers in Residence’ invited to interact with Dr. Traci Kelly’s monoprint installation Feeling It For You (Perspective) exhibited in Nottingham Trent University’s Bonington Gallery. The Seers in Residence programme engages four researchers: Emma Cocker, Ben Judd, Simon Cross and myself to respond according our own research and practice. Working with photography and text, Lee concentrated on surfaces and explored metaphors of knowledge and imagination.</p

Integration of radar altimeter, precision navigation, and digital terrain data for low-altitude flight

Avionic systems that depend on digitized terrain elevation data for guidance generation or navigational reference require accurate absolute and relative distance measurements to the terrain, especially as they approach lower altitudes. This is particularly exacting in low-altitude helicopter missions, where aggressive terrain hugging maneuvers create minimal horizontal and vertical clearances and demand precise terrain positioning. Sole reliance on airborne precision navigation and stored terrain elevation data for above-ground-level (AGL) positioning severely limits the operational altitude of such systems. A Kalman filter is presented which blends radar altimeter returns, precision navigation, and stored terrain elevation data for AGL positioning. The filter is evaluated using low-altitude helicopter flight test data acquired over moderately rugged terrain. The proposed Kalman filter is found to remove large disparities in predicted AGL altitude (i.e., from airborne navigation and terrain elevation data) in the presence of measurement anomalies and dropouts. Previous work suggested a minimum clearance altitude of 220 ft AGL for a near-terrain guidance system; integration of a radar altimeter allows for operation of that system below 50 ft, subject to obstacle-avoidance limitations

Terrain classification for a quadruped robot

Using data retrieved from the Puppy II robot at the University of Zurich (UZH), we show that machine learning techniques with non-linearities and fading memory are effective for terrain classification, both supervised and unsupervised, even with a limited selection of input sensors. The results indicate that most information for terrain classification is found in the combination of tactile sensors and proprioceptive joint angle sensors. The classification error is small enough to have a robot adapt the gait to the terrain and hence move more robustly

Geo-environmental mapping using physiographic analysis: constraints on the evaluation of land instability and groundwater pollution hazards in the Metropolitan District of Campinas, Brazil

Geo-environmental terrain assessments and territorial zoning are useful tools for the formulation and implementation of environmental management instruments (including policy-making, planning, and enforcement of statutory regulations). They usually involve a set of procedures and techniques for delimitation, characterisation and classification of terrain units. However, terrain assessments and zoning exercises are often costly and time-consuming, particularly when encompassing large areas, which in many cases prevent local agencies in developing countries from properly benefiting from such assessments. In the present paper, a low-cost technique based on the analysis of texture of satellite imagery was used for delimitation of terrain units. The delimited units were further analysed in two test areas situated in Southeast Brazil to provide estimates of land instability and the vulnerability of groundwater to pollution hazards. The implementation incorporated procedures for inferring the influences and potential implications of tectonic fractures and other discontinuities on ground behaviour and local groundwater flow. Terrain attributes such as degree of fracturing, bedrock lithology and weathered materials were explored as indicators of ground properties. The paper also discusses constraints on- and limitations of- the approaches taken

Fast approximation of visibility dominance using topographic features as targets and the associated uncertainty

An approach to reduce visibility index computation time andmeasure the associated uncertainty in terrain visibility analysesis presented. It is demonstrated that the visibility indexcomputation time in mountainous terrain can be reduced substantially,without any significant information loss, if the lineof sight from each observer on the terrain is drawn only to thefundamental topographic features, i.e., peaks, pits, passes,ridges, and channels. However, the selected sampling of targetsresults in an underestimation of the visibility index ofeach observer. Two simple methods based on iterative comparisonsbetween the real visibility indices and the estimatedvisibility indices have been proposed for a preliminary assessmentof this uncertainty. The method has been demonstratedfor gridded digital elevation models