Autonomous Attentive Exploration in Search and

Autonomous Attentive Exploration in Search and Rescue Scenarios Andrea Carbone, Daniele Ciacelli, Alberto Finzi, Fiora Pirri ALCOR Lab, DIS, Universit`a di Roma “La Sapienza” Via Salaria 113, I-00198 Rome, Italy {carbone,finzi,pirri}@dis.uniroma1.it

Abstract. In task-oriented exploration a robot has to direct its sight and delving towards the most promising regions of the environment, according to the task, in order to optimize its search. If the goal is dynamically set on the basis of what it is perceived, attention plays a crucial role, as it allows to combine fast glancing with accurate analysis, enabling the robot to quickly jump to conclusion by selecting the interesting spots in the environment requiring a further analysis. We present a task-oriented attentive exploration system designed for an autonomous rover working in rescue scenarios. The visual-attention process combined with the simultaneous localization and mapping one guides the robot search through an incremental generation of a global saliency map obtained according to transportation processes. Interesting features diffuse streams of particles warming up those map areas they pass through, in so generating hot regions that result in optimal vantage points for the robot to observe the salient spots glanced at while searching. We show the effectiveness of the approach by providing experimental results and comparisons.

1

Introduction

We present an approach to task-driven attentive exploration suitable for autonomous mobile robots involved in search and rescue operations. The main task of a rescue and search autonomous robot is to explore an unknown environment and find specific features concerning rescue operations. Attention, as the main attribute of the required situation awareness[15], is a crucial feature for the robot to find its own representation of the surroundings and generate suitable search strategies. Visual attention [7] usually refers to the ability of a vision system to rapidly detect salient locations in the visual scene. In this setting we extend this concept to the environment a robot is exploring to derive a global saliency map whose role is to drive the robot towards regions that optimize its vantage point. In our approach, the incremental construction of the saliency map works as a generative response to the environment as it leads to exploration choices propagated by perception. Attention determines, indeed, an emerging behavior which pulls the robot towards a bottom-up knowledge of the environment. Traditional approaches to robot exploration problems focus on geometrical solutions based on metrical maps (see [1, 22] for a review on Bayesian approaches to simultaneous localization and mapping and robot planning). Exploration strategies are, thus, mostly used to compel the robot to maximally cover the environment. A typical strategy

is to visiting the nearest unexplored place [25]. To avoid non-conspicuous exploration some approaches have incorporated decision-theoretic mechanisms to compute some suitable utility function related to the scanning process [13, 19]. Recently [17] has proposed a vision-based solution to the SLAM problem. SLAM-based exploration would be a satisfactory behavior when no other specific task is required, but the map of the environment. However, when the main task is to finding specific items such as victims, people, or particular objects in the environment, then it turns out that all the robot behaviors must be plied at the service of attention, mostly obtained via visual perception. Thus the exploration path has to adapt to the locations suggested by visual inspection. Attention-based control is an emerging issue for vision-guided mobile robots. Several approaches in literature address the problem of feature extraction to support task execution [10], localization, mapping, and navigation [11, 12, 16]. For instance, in [10] an attentive behavior is learned by pairing actions and image features. Attention basedcontrol have been also considered in [11, 12] where the exploration is visually-guided by the direction having the largest expected information gain. Other approaches, can be found in [4], in [24] and [14, 6], based on feature extraction for self-localization and topological navigation. A long term memory for feature integration during attentive exploration is proposed in [18]. However, so far, most of these approaches have not considered simultaneously navigation, mapping, feature extraction, and saliency map construction. In our approach we combine the metric information coming from sonars and dead reckoning with the saliency values extracted form visual features into a global saliency map, used to control the robot search. In this way, the robot extends the local attentional shift of a gaze, involved in the observation of a scene, to the global environment, and infers a search strategy. Robot exploration relies on the incremental construction of a world-centric saliency map that continuously integrates and stores the saliency values produced by the visual attention during the exploration. If salient locations in the visual scene represent interesting directions for the gaze, salient locations in the global map stand for interesting observation points for the robot, that is, places where it is convenient for the robot to make observations and take a decision on the real nature of the elements observed. The incremental generation of the global saliency map is obtained according to an advection-diffusion dynamics. The salient locations selected by the visual analysis are considered as the sources of particles spreading around, mainly in the direction of the robot, and transportation processes distribute on the map intermediate saliency locations. Particles concentration indicates “hot view points”. In this way, the emitting interesting objects generate streams of “hot regions” defining interesting directions for the robot explorations. In the rest of the paper we detail the motivations and the basic techniques of this approach.

2

Attentive Search and Saliency Map

The notion of saliency map plays a crucial role in the feature integration theory [23], one of the most influential psychological models of human visual attention. According with this model, in the first steps of visual processing, several primary visual features are processed and represented with separate feature maps that are later integrated in a

saliency map. The saliency map represents the conspicuity or saliency at every location of the visual field by a scalar quantity. This map can be accessed in order to guide the selection of attended locations based on the spatial distribution of saliency: the maximum of the saliency map defines the most salient image location to which the focus of attention should be directed. In the standard definition, the saliency map encodes the local conspicuity in the visual scene. Here, we employ a slightly different notion of saliency map where the saliency values, gathered in the visual scene, are mapped into global locations of the planar map of the external environment. We close relate the robot search within the unknown environment to the orienting process for eye (pan-tilt) and head (the whole robot) movements (see [8]) when exploring a visual scene. In other words, we maintain a global saliency map that is a world-centric planar map superimposed on the metric map where the current boundary and obstacles are represented as an occupancy grid. The saliency map labels with values of interest the metric map generated by a simultaneous localization and mapping (SLAM) process [22, 5]. The “hot regions” of this map represent places where it is more convenient for the robot to move to in order to make optimal observations and take a decision on the real nature of the elements observed. The global saliency map is exploited to guide the exploration: the robot search is pulled by the interesting regions generated in the attention process; once an interesting region has been visited and an interpretation for its content is obtained, it is cooled down and the robot continues with its exploration along the hot paths. The incremental generation of the global saliency map is obtained according to an advection-diffusion dynamics. Given the salient locations selected by the visual analysis, the advection-diffusion process allows us to distribute in the map intermediate saliency locations representing interesting observation points. This will be illustrated in Section 3. The global saliency map receives its inputs from the preattentive, parallel feature extraction stage. In our system, the vision decision process identifies the interesting cues in the image according to a smooth analysis (i.e. with a greedy parameters setting) based on textures and shape features. These are relevant features during search for victims in a rescue domain (textures and shape allows to detect skins and bodies). The preattentive shape-texture analysis performs a preliminary discrimination between smooth-flat elements and pseudo body shapes and between background colors and skin or pseudo-skin textures. This analysis is committed to regions earlier screened by a k-mean clustering carried on the 3D-map, and then assessed via two learned mixtures of Gaussians, one for the shapes, using the 3D image and one for the textures using the RGB image (this is an extension to 3D images of the approaches in [3] and [2]). According to specific thresholds for each of these components of attention, each region is labeled by a suitable combination of the obtained weights. The winning regions are then used to build the saliency map.

3

Saliency Map Generation

To distribute the saliency values (gathered through the analysis of the visual scene) in the global saliency map of the environment, we have to put saliency “in perspective”, not only geometrically (i.e. mapping local saliency values into the global metric map),

but also considering the interest of a position with respect to the task we are executing. Indeed, in the planar map a region of the space is interesting for the exploration as an observation point, that is: a location is salient if, from that position, a salient object is visible and reachable. In the approach we propose here, the saliency values are distributed on the global map through transportation processes. We consider the interesting elements as a source of particles spreading around, mainly in the direction of the robot. Therefore, the hot regions are those where more particles are concentrated. The saliency map can thus be obtained as a transportation process similar to advection-diffusion, and derived on the basis of the saliency weight, the region dimension and the position of the interesting elements detected, the robot current position and the metric map grid. The interest of the advection diffusion process for attention modeling is in the fact that it allows to model optimal vantage points through the heat distribution. The hottest regions obtained by the particle diffusion, are those on which the maximum number of particles cumulated. Hence, according to the drift, these regions turn out to be the best vantage point for observing the most interesting elements glanced at. See Figure 1.

(a)

(b)

Fig. 1. Figure (a) shows the particles diffusion from 5 interesting points that have been glanced at by the robot. Figure (b) shows the temperature map as obtained by particle transportation. The yellow-red-orange regions indicate the hot regions where the robot should lead to, to optimally look towards the interesting regions.

To represent the transportation of particles from the emitting interesting object towards the robot, and thus obtain the hot regions, we have modeled the process as a Markov chain, a general case with respect to biased random walks. Our aim is to model the drift of particles from the interesting object in position x0 = (x0 , y0 ), towards the robot, in such a way that the flux does not encompasses the robot position xR = (xR , yR ) and, at the same time, particles distribute also orthogonally with re-

spect to the direction towards the robot, in so spreading in the visible directions from the robot vantage point. At the same time this cloud of particles should constitute an attraction zone for the robot, in order to put it in the condition to closely approach the most interesting objects in the current scene. Consider one of the regions r = [w, c, V ] preselected by attention, where w is the weight of the region, c is the region centroid position in real world coordinates, as given by the map M , and V is the volume of the convex hull including the region. Let x = (x, y) be the two-dimensional projection of c = (x, y, z). The particles at each step move either in a direction α toward the robot, or in the orthogonal directions ±β. The simulation should thus collapse at precisely the time tR at which the particles hit the robot current position. In the following we shall consider the projection of the diffusion on the plane, despite we assume that, if something has been glanced at, it might be at a certain height, and it might not necessarily be reachable from the robot current location along the particles trajectory. In fact, we shall consider in the next section the problem of computing a path to reach the object and observe it from a reasonable distance, to decide whether it copes with the current task or not. The main trajectory of the particles is along the line joining the glimpsed object and the robot (see the black line on the particle drift in Figure 1). Notice that if the robot can glance at the object there must be at least a portion of the object which is visible, hence there is a straight line of sight from the robot head to it. Particles are moved along the trajectory of a state step δx, defined as follows, where x = (x, y),   p δαx(t + 1) = ft (x) b1 + λmax (t)   (1) p δβx(t + 1) = ft (x) b2 + λmin (t) Here α is the trajectory angle, while β is the angle of the orthogonal direction to the trajectory; λmax (t) and λmin (t) are, respectively, the greatest and smallest eigenvalues of the covariance of the particle cloud at time t (see below), b1 and b2 are positive constants, finally, ft is the joint pdf at time t of the random bivariate normal variables (x, y), with mean the trajectory center, variance σx = 1/do (C, t), σy = dr (C, t), with do (C, t) the distance of the center C of the particle cloud at time t (see below) from the emitting region and dr (C, t) the distance from C to the robot, at time t, and the correlation ρ is zero. The particles step, as defined in (1), models a particle flux balancing the initial saliency weight and the strength of the drift. It is easy to show that if dr (C, t) < do (C, t) then δαx(t + 1) < δαx(t). For, consider the bivariate: 1 p

ρ2 )

1 exp(− Q(x, y)) 2

(1 − 1 Q(x, y) = (µσ (x)2 − 2ρµσ (x)µσ (y) + µσ (y)2 ) (1 − ρ2 ) z − µz µσ (z) = ( ) σz 2πσx σy

at C = (µx µy ) we have the highest value for ft , and the cloud of particles at time t forms an ellipse whose major axis is in the direction of λmax . Hence the particles close to the center are those which are moved faster at each time step. Furthermore because

Fig. 2. The sequence of pictures shows some steps of the attention driven exploration process. In the experiment from which the sequence is taken, 10 interesting objects were glanced in the scene. The robot attention process were able to reach 8 of them and observe them closely.

σx and σy decrease with time, as the particles get closer to the robot and further from the emitting region, the ellipse get smaller and smaller and there exists a t such that the step values reduce to ft (x) getting closer to zero. The set of particles at each time step t are obtained as follows. At time t0 the particles are distributed according to the initial probability N (µt0 , I h), where I is the identity matrix, h is a factor depending on the metric scale of the map, and µt0 is the position xt0 = (x0 , y0 ) of the specified interesting region r at time t0 , as obtained by the attention process. More specifically, at time t0 , there is a number K = kV N0 w of particles in position xt0 = (x0 , y0 ), which is the 2D position assigned to the region r by the attention process, N0 is a constant that we set to 500, while V and k are regions parameters depending on the feature extraction. The initial state for each region is, thus, obtained by sampling kV N0 w particles from N (µt0 , Ih). Notice that there might be several regions detected, depending on the current field of view of the robot (for example, in Figure 1 there are five regions glanced at by the robot, at time t0 ). The successive states at time t1 , . . . , tN are obtained according to a transition matrix determining the probability, for a particle, to move towards a specified direction from its current position. In particular, at each time step ti , each particle can move of a space step δx(t) (according to equation (1)) from the current particle location xt = (xt , yt ) to one of the following three locations: → s1 = (xt + δβx(t) cos(β), yt + δβx(t) sin(β)); → (2) s2 = (xt − δβx(t) cos(β), yt − δβx(t) sin(β)); → s3 = (xt + δαx(t) cos(α), yt + δαx(t) sin(α)); → →

→

Here s1 , s2 and s3 are the position reached, respectively, after a step move in the orthogonal directions −β and β, and in the direction α of the robot, and are distinguished from the transition states, in the sense that they give the instantiated location of the states. On the other hand, the transition matrix models the particles direction transitions so, for example, if at time t the direction of the particle is α then with probability p(st+1 = w|st = α) it will change its direction towards w ∈ {α, ±β}. The transition matrix has been obtained from a system of linear inequalities accounting for the minimization of the time rate of the particles to reach the robot, and the maximization of the particles spread, purporting the ratio conspicuity/exploration. Because the transition matrix P is homogeneous and has just three states then the stationary distribution is obtained as π = Pn , in particular the steady distribution obtained is π = [1/5 1/5 3/5]. Note that the stationary stochastic vector models the frequency a given state will be visited. This can be easily shown to be equivalent to sampling, at each time step t, 1/5 of the particles from the distribution N (µt−1 , σt−1 ) to transit to state β, 1/5 to transit to state −β and 3/5 to transit to state α. Here µt−1 and σt−1 are the mean and variance estimated with respect to all the particles sampled from the previous step. If a particle q will transit from the current state si to the state sj then its real location will be given according to equation 2. Since the process is ergodic, all the states reach the stationary distribution at once and because the process is biased with respect to the third state, more particles are directed towards the robot. From the observations above it follows that the eigenvector associated with the greatest eigenvalue λmax will progressively turn the particles drift

Fig. 3. The above sequence follows the one shown in Figure 2. At each step the robot glances at the surrounding and the particle emission begins. The robot compute the path to the hottest point and reach it.

toward the same direction as α. Furthermore, as noted above, the space step will collapse, by equation (1). Therefore, while the overall mean of the particles move towards the robot, the process initially scatters out (and the covariance of the particles increases) until it reaches a maximum at the trajectory mean and then the particles velocity decreases until becoming close to 0. At that point the covariance of the particles tends to stabilize, the process converges and terminates in a number of steps proportional to the distance between the robot and the region. Each particle has value the initial weight of the region, note that when a particle moves it transports its value to the next cell but will leave (like a sort of stream) a copy of its weight in the cell it is departing from. Finally, note that the probability of a particle to be in position (xt , yt ) at time t depends both on the the Markov chain and the sampling process, while the value of the particle depends only on the region it spread out from.

4

Approaching the Target and Update

We define the map temperature Tt at time t as the weight of each cell in the map which is free at such a time. Attraction and repulsion of the robot towards interesting or less interesting areas is due to the temperature, hottest cells attract the robot. In particular the superposition of particles in the diffusion process creates hotter regions, because whenever two diffusion processes are superimposed the weights transported are cumulated over the cells, see Figure 1. Note also that because the diffusion process, once passed the trajectory middle point, reduces at each step its spread strength and only the last emitted particles arrive in proximity of the robot, then the robot location is not the hottest region, which is eventually around the center of the trajectory. In fact, given a set Q = {q1 , q2 , . . . , qk } of interesting elements having weights wi , 1 ≤ i ≤ k, with small standard deviation and a region R on the map, it can be shown that if R maximizes the

number of trajectories passing through it, and not ending on it, then R will also maximize the number of particles, therefore R will be the hottest region and furthermore it will be the region in a best position with respect to most of the elements in Q. Note, however that R can be a region maximizing the number of particles, yet not being the region that maximizes the number of trajectories passing through it, this is the case if the standard deviation of the weights is high and one of the qi ∈ Q dominates all the other elements, that is Ki > Kq for all qj ∈ Q, i 6= j and for all t ≤ tN . Let H be the hottest point in the current saliency map. If H is reachable, then a path is computed, avoiding possible obstacles (note, in fact, that the metric map is already computed for all the visible regions) and the robot reaches it. Then H is cooled down with its surrounding region. Now, because the robot follows a greedy strategy, H will be the hottest location as an absolute maximum, however considering a circle centered in the hottest point H and with radius r the region will have a varying temperature, therefore we consider the mean temperature TH of the region surrounding H, the original value before heating T0 , and a cooling parameter K, the rate of heat loss is: dTH = −K(TH − T0 ) dt In order to determine the area of the cooling region and to smooth the cooling so that it does not go lower than zero, we shall consider a circle centered in H which expands with time. Assuming T0 = 0, that the heat loss is proportional to the increase of the cooling area, and given that the circle area derivative is 2πr(t)dr/dt, we get: KTH dr =− 2πr(t) dt Hence at each time step the circle radius increases of the same amount the temperature of the corresponding area decreases, and K is such that the temperature reaches at most the zero. The results of the cooling process of the exploration illustrated in Figures 2 and 3 is illustrated in Figure 4 on the right.

5

Experiments

In this section, we discuss the effectiveness of the proposed exploration methodology and compare this with respect to alternative approaches. The approach described above has been implemented and tested on a real robot. We implemented the system on a Pioneer 3DX from ActivMedia with an on-board low consuming laptop (Centrino ASUS M3000, 1.6GHz) that hosts navigation, map building, and the on-board sensors control. An additional PC ([email protected]) for remote control is also used for image processing. Two different camera systems are mounted on top of the robot at a height of 55 cm on a pant-tilt head PTU-4617, from Direct Perception. A laser range finder HOKUYO, is mounted on the pan-tilt between the two cameras. Video streaming and single frames are acquired through the Image Acquisition Toolbox from Matlab.

Fig. 4. A final saliency map after 10 min. the resulting temperature map shows that the robot could still reach two more interesting points.

5.1

Saliency-based Exploration

The proposed saliency-based exploration method is based on a continuous and incremental construction of the global saliency map. The advection-diffusion model permits a fast and local update: for each interesting point detected, the associated advectiondiffusion process takes about 0.1sec to stabilize and distribute saliency values on the planar map. The computational cost rises linearly with the number of saliency points detected at the same time. The best region choice cost is about 0.05sec. We tested the approach by comparing pure metric exploration (where attention and saliency map are not considered during the exploration) with respect to the attentive exploration. We experimented these two settings on areas of 40 × 40m2 (rescue arenas [21, 20]). The exploration time was 20 minutes. We performed 20 tests in the two modalities. For each test, there were 20 objects (mannequins representing victims in a rescue scenario) to be discovered. Each object was associated with a salient area to be inspected, we called this topological area a salient region (SR). For each test we considered: i. the percentage of the explored surface; ii. the number of salient topological regions (SR) inspected (i.e. approached and analyzed) with respect to the total; iii. the time spent inspecting salient regions. The results are summarized in Table 1.

Surface (m2 ) Explored (%) SR Visited Time in SR (min)

Metric Exploration Attentive Exploration 40 40 70.1 8.7/20 6.2

Table 1. Attentive vs. Metric Exploration.

55.7 12.8/20 9.6

The table shows that metric exploration covers a larger portion of the arena, but attentive exploration seems more effective. Indeed, more salient areas are explored and inspected in this modality, thus more time is focused in the analysis of interesting areas.

Fig. 5. Figure (a) shows the potential field generated by two saliency points detected by the robot and a final attractive point that reduces both reachability and visibility. Figure (c) shows the VI-based explorative behavior given two salient locations. Figures (b) and (d) depict the robot trajectory (white line) during, respectively, a PF-based and a VI-based exploration.

5.2

Comparisons

We compare the proposed saliency map generation process (and the associated exploration strategy) with respect to alternative approaches adopted in literature, i.e., value iteration (VI) and potential fields (PF). Potential Fields: Potential fields [9] are well-suited to drive the robot navigation once a goal is defined, but the applicability of this method to robotic search towards the interesting observation points provided by visual attention is not obvious since the attentive exploration continuously provides multiple and unstable targets (the salient locations). Here, interesting regions and obstacles can be associated to, respectively, positive and negative charges so that the obtained field attracts the robot towards interesting places avoiding obstacles; in this context, saliency in the planar map can be assimilated to the

value of the potential field: low values correspond to attractive areas. This method is fast (e.g. a saliency map of 150 × 150 cells can be built in less then 0.1sec), but not satisfactory given the notion of saliency we are interested in. First of all, the (electrostatic) attraction of a location seems not well-suited to characterize interesting observation points since the analogy potential/saliency seems not natural (e.g., in the absence of interesting regions in the map, the potential of the cells would be zero, that is, given the interpretation of saliency as low potential, we obtain maximal interest for each location without any stimulus). Furthermore, potential fields can determine non-local influence of the saliency/potentials not directly related to the current observations of the agent. Thus, the attractive points do not necessarily characterize good positions for the exploration task. For example, Figure 5 (a) illustrates a situation where two saliency points have been detected and the robot exploration is driven by potential fields. Here the minimal potential point (blue zone) drives the robot into a room where the two interesting points are not visible anymore and their reachability is reduced. To avoid these situations, the potential fields should be tailored with respect to the robot position, the visible salient locations, obstacles, etc. but this seems too complex. Value Iteration: An alternative approach to attentive exploration can deploy value iteration. In this case, we can define a decision theoretic problem where each detected interesting region can be associated with a utility value equivalent to its saliency. Thus, for each point of the planar map already explored we can calculate the associated saliency/utility value deploying a value iteration algorithm. In other words, we can associate to each cell the discounted expected reward/saliency that can be gathered if we follow the optimal strategy from that position. This method is computationally feasible (e.g. a saliency map of 150 × 150 cells can be built in about 0.2sec). However, also in this case, the approach is not local (i.e. whenever new salient elements are discovered a complete update of the saliency map is needed). In addition, we cannot get the aggregated contribution of several saliency regions, i.e. given two interesting points A and B (see Figure 5 (b)), if the optimal strategy for a cell x is to move towards A, then the saliency value for x is the expected discounted reward associated to A, that is independent from B. For example, in Figure 5 (b) the VI-based exploration drives the robot directly towards the best target without looking for better intermediate observation points. That is, the approaching phase is eliminated and instantaneous (and unstable) saliency values determines drastic choices. Finally, the so calculated saliency values are strategy-dependent, while, on the contrary, we want a saliency map wellsuited to give fast guidance to a reactive exploration process. Tests: We have implemented these two alternative exploration methods and tested with the experimental setting introduced in Section 5.1: we considered again 20 salient objects in 40m2 , 15 tests, 20min each (Figure 5 (b) and (d) illustrate some tests deploying, respectively, PF and VI based exploration). The results are summarized in Table 2. The results obtained for of PF and VI are very close, both these methods, as expected, can focus the robot search towards salient regions. However, the advection-diffusion (AD) method seems more effective in focusing the time spent for the target approach and object inspection. Furthermore, the number of false positives FSR (false salient regions) is reduced in AD. Indeed, both PF and VI are more sensitive to the continuous change of

Surface (m2 ) Explored (%) SR Visited FSR Time in SR (min)

Attentive (AD) Expl. Attentive (PF) Expl. Attentive (VI) Expl. 40 40 40 55.7 12.8/20 2.4 9.6

59.3 10.4/20 3.6 7.8

57.2 10.8/20 3.3 8.2

Table 2. Attentive search strategies: comparing advection-diffusion (AD), potential fields (PF), and value iteration (VI). Here SR stands for salient regions and F SR for false salient regions.

saliency points: in PF and VI the saliency in the global map is recalculated at each step, then the emergence of new goals can determine distraction and oscillatory behaviors. For example, Figure 5(b) depicts an abrupt trajectory change during a PF-based exploration due to the detection of a new attractive salient point. This effect is more evident with PF that requires ad-hoc potential functions to be effective for both navigation and observation.

6

Conclusions

We have presented a task-based attentive exploration system designed for autonomous robots working in rescue scenarios. In our approach, the exploration strategy is the result of the cooperation between visual attention and simultaneous localization and mapping. The saliency values extracted form visual features are integrated into a global saliency map superimposed over the metrical one. This map determines interesting view points for the robot search and can be used to guide a task-oriented exploration. The incremental generation of the global saliency map is obtained according to an advectiondiffusion dynamics. The salient locations selected by the visual analysis are considered as the sources of particles spreading around, mainly in the direction of the robot, and transportation processes distribute in the map intermediate saliency locations (representing interesting vantage points to explore the environment). The system have been implemented and tested in real-world experiments by comparing the performances of our attentive search method with respect to pure metric exploration (where exploration is not driven by saliency) and alternative attentive exploration strategies. The results obtained demonstrate that the attentive behavior significantly enhances the exploration effectiveness.

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