Many challenging conditions can be described as low-visibility. Visibility is technically defined as the distance one can perceive a high-contrast object [104] and typically refers to the attenuation of the signal by the atmosphere in the distance between the object and the sensor. As shown in Figure 2.6 and described in Section 2.1.3, sensors perceive the environment and provide data to the perception system. In real-world conditions, there is no control over the contrast between objects in a scene or the atmosphere of the environment, yet these contribute to the signal that is measured by the sensor. Similarly, the sensor capabilities will contribute to what portion of the available signal is measured and how the signal is developed into data for the perception system. For this work we do not contribute to the hardware design of the UGV system and therefore, the sensor function cannot be modified or replaced. Therefore, for the context of this work, the visibility of a scene refers to the ability to resolve contrast between distinct environmental features in the scene from the measured sensor data.
Figure 2.10 shows a scene (B) emitting an electromagnetic signal (C) that is measured by a sensor (F). For simplicity, the electromagnetic (EM) signal from the scene is shown as five discrete lines and the sensor is composed of two individual measurements to produce two data values. The scene is said to be visible if the contrast of the scene can be resolved in the contrast between the output sensor data.
Figure 2.10 – Contributions to the visibility of a scene in the output sensor data.
ence of atmospheric conditions that attenuate the EM signal, poor sensor capability to perceive contrast in the EM signal and/or the internal sensor processes that lose information when they convert the EM signal to data. Figure 2.10 shows the rela- tionship of all the factors that contribute to visibility including:
(A) the illumination of the scene from a source of EM energy (yellow). (B) a measurable contrast between parts of the scene (green).
(C) the intensity of the electromagnetic (EM) signal from the scene (red) as a com- bination of:
• EM energy radiated from the scene,
• EM energy from the illumination reflected by the scene.
(E) the attenuation of the EM signal due to other occlusions, (F) the capability of the sensor to capture EM energy including:
• the field of view (e.g. in the figure, the sensor only captures 4 of the 5 EM beams.),
• resolution (e.g. in the figure, two EM beams contribute to a single mea- surement.),
• spectral range, • sensitivity.
(G) the conversion of the EM input signal into output data by physical and electronic processes of the sensor, for example:
• amplification, • discretisation, • quantification, • compression.
Low-visibility due to the environment can be global, with similar visibility conditions in every direction (e.g. night or dense fog as viewed by a visual camera). In other common cases (e.g. presence of smoke or dust clouds), visibility conditions can be locally variable, in position, density, and over time, which is more difficult to model. Note that the visibility of a scene can be improved by expanding the capabilities of the sensing suite through a greater number of sensors and an increased range of sensing modalities. Specifically, more sensors will increase the field of view and different modalities provide a greater spectral range of the combined sensor suite. The wider sensing space provides a greater opportunity to discriminate contrast in the sensor data from the scene and less chance that relevant signals will be blocked through attenuation.
Low-visibility conditions are challenging for perception systems because typically sys- tems are designed with assumptions that a scene will be visible. Relevant features
Figure 2.11 – Atmospheric absorption characteristics of the atmosphere as a function of frequency [110]. The visibility of a scene is strongly affected by the atmosphere and its affect on the particular EM frequency of the signal.
in the operational environment are difficult to observe or not observed at all. For example, the Boss UGV laser sensors provided inappropriate data when a dust cloud obscured and attenuated the signal from solid objects behind the cloud. The subse- quent sensor data did not contain relevant features from the scene that were required for the perception system to produce an accurate representation of obstacles. The MER UGV perception system failed when the image data was too homogeneous to identify distinct features due to poor illumination or lack of contrast in the scene in the visible spectrum when fine dust covered the ground.
In most state-of-the-art UGV systems, failures in perception are detected after the failure when it impacts a higher-level process (e.g. when the localisation is inaccu- rate or the UGV stops moving unexpectedly). The identification of the disturbances that caused the failure typically occur off-line such as in the system reports of the
MER [141] and Boss [82] UGVs where environmental conditions such as darkness and dust were respectively identified as the cause of failures. Once identified, these situations can be corrected by developing appropriate hardware and/or software to handle the specific disturbance i.e. reducing the causes of failure through a better sys- tem design. Examples of these solutions are presented and discussed in the following Section 2.3.3