Combining Dynamic Frontier Based and Ground Plan Based Exploration:

Transcription

1 Combining Dynamic Frontier Based and Ground Plan Based Exploration: a Hybrid Approach Jens Wettach, Karsten Berns {wettach, berns}@cs.uni-kl.de Robotics Research Lab, Department of Computer cience, University of Kaiserslautern, Germany Abstract Exploration of unknown environments is a key feature of autonomous service robots since a reliable map of the working space is a prerequisite for any navigation task. In the past a serious of exploration strategies has been developed that operate on different kinds of maps. This paper presents a new hybrid strategy which exploits the difference of floor and ceiling layout in the first place and uses a dynamic extension of the well-established frontier based approach as fallback in situations where the acquisition of room extensions is impossible. 1 Introduction According to [1] an integrated exploration process consists of three tasks that have to be performed simultaneously: mapping, localization and motion control. The first two of them have been examined exhaustively as the LAM problem during the last two decades. Motion control concerns the question where to move next to acquire as much new space as possible (the core problem of exploration) and how to get there as fast as possible (navigation). A common layout of an exploration process has been defined as next-best-view (BV) exploration by [2]. The BV position is calculated by evaluating the already known information about the working space (incomplete global map). After approaching it the robot acquires a new partial map and thus updates the global map before the next iteration is started. olutions to this problem mainly differ in the kind of map (e.g. grid map or topological map) and in the strategy of generating BV candidates and of selecting the most promising one. [3] has presented a straightforward strategy that exploits the length of frontiers between known and unknown space for scoring BVs. [4] has extended this approach to a dynamic version that provides a performance increase due to online map updates and the possibility to leave the known space (safe region) during navigation. More sophisticated solutions balance two or three criteria, e. g. expected information gain, travel cost and chance of relocalization (reducing map errors) [5]. In this regard [6] combines information gain, travel cost and dispersion of BVs, but evaluates both a grid map and a geometric map of floor and ceiling to determine these parameters. The core idea is that areas either visible only at the ceiling (space covered by tables, chairs, etc.) or at the floor (hidden space behind walls above open doors) are of special interest during exploration. Finally the method of combining several key numbers for scoring BVs is an open problem. All solutions mentioned so far rely on ad hoc functions, i. e. a weighted sum of parameters. On the other hand [7] has developed a sophisticated scoring function based on information theory which computes the difference between ideal and expected information gain. [8] has evolved this strategy to the concept of multi-criteria decision making (MCDM) and compared different implementations of it based on an existing LAM and navigation system for search and rescue robots. A common drawback of many existing exploration systems is a limited applicability in everyday working environments of mobile robots. ometimes the layout of the working space is restricted regarding the shape and number of obstacles, sometimes dynamic objects as walking humans are blanked out, sometimes the localization problem is neglected. Hence there is a demand for a dependable integrated exploration approach that works reliably in real world scenarios without user interaction or system degradation. The approach described in the paper at hand tries to satisfy this demand using existing solutions for LAM and navigation while combining the previously developed strategies of [4] and [6] into a hybrid system. It will turn out that the individual benefits of two solutions increase the reliability of the exploration process in difficult situations, e. g. highly cluttered areas. The remainder of this paper is organized as follows: section 2 summarizes the basics (sensors, LAM, navigation) for the exploration process. The details about the proposed hybrid approach are explained in section 3. Experiments in simulation and reality are discussed in section 4. Achieved results and pending improvements are subject of section 5. 2 LAM and afe avigation The exploration strategy has been implemented on MAR- VI, a mobile indoor robot for office as well as industrial environments. Its sensor system comprises two planar

2 laser scanners at front and rear, a circumferential belt of 20 ultrasonic sensors and a rotating 2D scanner (vertical scan plane) that provides 3D distance data of the motion corridor. Mapping and exploration use the accurate data of the scanners, ultrasound is only used additionally for obstacle avoidance. LAM relies on the well-established DP-LAM approach with small modifications. It implements a grid map based particle filter for localization. After each LAM loop its most probable pose is used to correct the imprecise odometry based pose estimates. These online map updates facilitate leaving the safe region during BV approach. More information about this topic can be found in [4]. ince the exploration process is a hybrid approach two kinds of maps are generated. First, the grid map of the LAM unit is filled continuously with distance information from all three scanners. Besides at each BV a 360 panorama scan is acquired with the 3D scanner if there is enough space around the robot. Thus the map is updated with the collected point cloud before BV calculation. The grid map is used for the frontier based exploration unit and for navigation during BV approach via an elastic band path analyzer. The ground plan based explorer exploits differences in the layout of floor and ceiling for selecting BVs. Therefore each 3D point cloud of a panorama scan is input for a RAAC based plane extraction using PCA and least square plane fitting. Its output are planes representing the core structure of the environment: walls, floor and ceiling. A feature extractor fuses this data into a set of polygons for floor and ceiling and thus generates two polygonal maps (see [6]). 3 A Hybrid Exploration trategy Figure 1 shows the statechart of the hybrid exploration system. According to the classic next-best-view approach it consists of three horizontal levels: map update via panorama scan, BV calculation either using frontiers in the grid map or differences in the polygonal map as result of the 3D environment reconstruction, and navigation towards BV. The concurrency of the two mid-level states represents the hybrid character of exploration: BV generation and scoring is executed via the polygonal map in the first place and grid map frontiers are only regarded in case of a failure. Reasons for that may be too less differences in floor and ceiling layout (e. g. in large unobstructed areas as hallways), or the impossibility of recording a 3D point cloud in highly cluttered areas (i. e. no updates of the polygonal map). The latter is due to safety: since the 3D scanner protrudes from the front side of the robot there has to be enough space around the robot to perform a 360 turn. Besides if walls are too close to the robot the 3D reconstruction may not work properly because only small parts of walls are extracted. All these cases benefit from the dynamic extension of the frontier approach: online map updates assure new information since the last BV step as soon as the robot starts to approach the current goal. Hence this exploration unit always calculates a valid BV: it may be suboptimal due to restricted information of the grid map compared to the polygonal map, but keeps the exploration process working. Furthermore the smooth switch between the two approaches facilitates a comparison of the power of each individual process: even in situations where a panorama scan is allowed the 3D reconstruction can be inhibited manually. In this case the state automaton performs pure frontier based exploration. On the other hand the fallback from exploiting the polygonal map to frontiers can be forbidden which means pure ground plan based exploration. ection 4 contains a comparison of pure frontier based exploration and the proposed hybrid ground plan based approach in a simulated office scenario. The results show how the hybrid strategy increases the performance and reliability of exploration. The most interesting component of all these exploration modes is the scoring of BV candidates c i. The most promising pose ĉ i of all candidates is calculated as the maximum of the evaluation functionf(c i ): f(c i ) = λ a area(p i )+λ u unknown_cells(c i ) λ c cost(c i ) λ o occupied_cells(c i ) λ dp distance_to_past_nbv(c i ) λ du distance_to_unreachable_nbv(c i ) (1) The area of a difference polygon p i with center c i marks the expected information gain in terms of the ground plan map, the number of unknown cells within a sensing circle of a given radius around c i represents the same aspect in terms of the grid map. If a 3D reconstruction and consecutive update of the floor and ceiling map has been performed both indicators are used. In case of a fallback to frontier evaluation the difference area is set to 0 for allc i. egative factors are the cost of the navigation path, the number of occupied cells in the sensing radius (accessibility of the BV) and the dispersion, i. e. proximity to all past BVs. Here one has to distinguish between BVs that actually have been approached in the past and those that turned out unreachable. This distinction is due to the strategy of approaching an BV: naturally the best BV candidate is selected as goal, but due to unknown areas or changing obstacle positions (closed doors, walking people) this goal may never be reached. o the past BVs are those where the elastic band navigator stopped the robot motion because either the goal was reached or estimated as unreachable. At this position the next panorama scan (or pure frontier evaluation) takes place. But if there is a remarkable distance between this position and the very goal the latter one is recorded as unreachable BV. This way BV candidates that are attractive e. g. due to a high value of expected information gain are excluded from fu-

3 Idle Activation Deactivation Running PanoramacanModeelect [Panorama can Allowed] [! Panorama can Allowed] Perform Panorama can [3D Reconstruction Allowed] [! 3D Reconstruction Allowed] Get BVs from 3D Reconstruction BV Poses Calculated/ [# BV Poses from 3D Reconstruction == 0] Get BVs from Gridmap Frontiers BV Poses Calculated/ [# BV Poses from 3D Reconstruction > 0] BV Poses Calculated/ [# Gridmap BV Poses > 0] BV Poses Calculated/ [# Gridmap BV Poses == 0] core BVs from 3D Reconstruction core BVs from Gridmap Frontiers o BV Found BV Found BV Found o BV Found Plan Path to BV o Path Found Path Found Approach BV BV Reached (Path Invalid/ [Replanning ot Allowed]) Path Invalid/ [Replanning Allowed] Figure 1: tatechart of the hybrid strategy combing ground plan based exploration (left states) and frontier based exploration (right states).

4 ture scoring to prevent the exploration from deadlocks. Experiments will highlight the importance of this approach. The scoring factors are calculated as relative values for each c i using the minimum over all c i for dispersion and the maximum otherwise. The weighting factors λ a,u,c,o,dp,du have been introduced to control the influence of individual parameters which is common sense of any ad hoc function [7] that combines several exploration aspects. aturally their values have to be chosen carefully to achieve reasonable exploration results (see section 4.1). 4 Experimental Results The proposed exploration system has been validated both in a simulated office scenario via the imvis3d framework as well as in its real world counterpart. ince the model is based on a construction plan of the real building the accuracy of the generated real world map can be directly assessed by an overlay of that map and one that has been generated in simulation without odometry errors (cf. fig. 6). 4.1 Experimental etup The experiments on the real robot have been performed with the same layout of accessible working space, i. e. open and closed doors. However, since it is a usual office scenario, sometimes doors connecting offices and hallway are opened and people are casually walking through the scene. These dynamic changes are an additional challenge of real world experiments apart from the localization problem. Besides the DP-LAM unit does not provide a correctly aligned global map because when the robot starts to move the first partial map is recorded and its orientation in the world reference frame is fixed for all future map updates. Therefore a compass dial is attached to all maps so that one can image how they fit to the applied scene. The setup of the scoring function 1 was the same for all experiments. The parameters had been chosen as (λ a,λ u,λ c,λ o,λ dp,λ du ) = (5,1,15,1,8,50). o unknown and occupied cells (i. e. the information of the grid map) have least influence, followed by difference of floor and ceiling map, distance to past BVs, path cost (here: path length) and distance to unreachable BVs. The high penalty of the latter ones is needed to prevent deadlock situations, e. g. keeping the robot away from a room that has been scanned partially through an open door which afterwards has been closed. hallway robot (start pose) hallway W E open doors meeting room chair tables kitchen stairway pillars 4.2 Comparison of Frontier Based and Hybrid Ground Plan Based Exploration In order to compare pure frontier based exploration and the proposed hybrid extension of the ground plan based approach the simulation experiments have been conducted without environmental changes and without odometry errors. Only Gaussian noise of laser scanner data is present for testing the respective filter algorithms. This way both exploration strategies are evaluated under the same controlled environmental conditions without any influence of random errors. 29, 30 28, 32 W E Figure 2: imulated office scene. Figure 2 shows the simulated environment. It consists of several rooms connected by an L-shaped hallway with an entrance area at its south-west side (where the robot is located). The rooms are connected to the hallway or adjacent rooms via doors (green areas) that may be opened or closed on demand. At the east end of the hallway is a stairway in the real scene. For reasons of simplicity it has been blocked by two half-size walls in this example. Most interesting is the meeting room with tables and a chair that has been made accessible from the hallway (left open door) and indirectly via the small kitchen at its right side (other two open doors). This is also a challenge for the loopclosing capabilities of the DP-LAM unit. 19, 20, 21 Figure 3: Result of frontier based exploration in the simulated office scenario: grid map (occupied cells = red circles), numbered BVs (blue) and navigation path (green). Figure 3 shows the grid map, navigation path and num-

5 bered BVs as result of dynamic frontier based exploration according to [4]. Occupied grid cells are displayed as red circles. The cell size is cm 2. Walls are represented by up to 3 adjacent cells even if they are scanned only from one side (e. g. the hallway) because of noisy distance data. Apart from that the recorded map can be regarded as ground truth due to the absence of any localization error. The test run took about 90 minutes with a travel path of 141 m and 36 BVs in total. To ease the comparison of all exploration test runs table 1 contains the respective key data. Test Run Duration Length BV 3D can FB () HGPB () HGPB (R) HGPB (RD) Table 1: Key data of exploration results: duration in minutes, path length in meter, number of BVs in total and those with panorama scans; FB = frontier based; HGPB = hybrid ground plan based; = simulation; R = real, static environment; RD = real, dynamic environment The exploration process has been started at BV 0 with the robot facing to the east (cf. fig. 2). First the entrance area is scanned (BV 1-4), then the south-east part of the hallway (BV 5-15). The large open space in the corridor makes the respective frontiers most attractive. One exception is BV 12 in the kitchen which marks the main drawback of using only known/unknown cell information. Looking through the open door makes this BV more attractive than those in the east end of the corridor. Afterwards the narrow space in the kitchen drives the robot back to the hallway before it reenters the kitchen at BV 16. This leads to two unnecessary and time consuming door passages. Thereafter the meeting room is entered (BV 17, 18) and left quickly (BV 19-21) because the spare space around the tables is not attractive for the Greedy search of big frontiers. Then the north end of the corridor is explored (BV 22-25), before the missing part of the meeting room is acquired at BV After that the algorithm terminates since there are no more valid BV candidates. Apart from the double entering of kitchen and meeting room the exploration path is reasonable and fits to what can be expected from this exploration strategy. Panorama scans have been performed at 25 BVs. o scan was possible at BV 10 (hallway), BV (narrow passages near doors of meeting room), and BV 26-28, 31, 32 (narrow space between tables and wall). Here BV 10 is an outlier because actually there is enough space for a panorama scan in the corridor. But sometimes sensor noise leads to casual false positives in the obstacle grid map filled by the 3D scanner and one close occupied obstacle grid is sufficient for preventing a panorama scan. Figure 4 shows the output of the new hybrid ground plan based exploration strategy applied to the same experimental setup as in the previous test run. In addition to navigation path, BVs and grid map both polygonal maps of floor (orange) and ceiling (blue) are shown. The process starts again in the entrance area. First the west (BV 0-2), south (BV 4-7) and east part (BV 8, 9) of the hallway are explored. In contrast to the frontier based test run the robot now returns to the north part of the hallway (BV 10) before leaving it the first time (BV in the meeting room). But then a similar failure as in the previous experiment happens: the robot returns to the hallway (BV 14, 15) instead of completing meeting room first and entering kitchen afterwards, i. e. there is again an unnecessary double door passing. In fact after completing the hallway the robot explores the kitchen at BV 16, 17 and then the rest of the meeting room (BV 18-24). At this point the algorithm stops since no valid (accessible) BV candidate is left. 22, 23 Figure 4: Result of hybrid ground plan based exploration in the simulated office scenario: grid map (red circles), floor map (orange polygons), ceiling map (blue ones), numbered BVs (blue) and navigation path (green). Compared to the frontier based test run it took only 25 BVs (fb: 36), but with a travel path of 211 m (fb: 141 m) and a duration of 64 min (90 min, cf. table 1). At 13 BVs no panorama scan was possible: BV 4 (close to door), 9 (close to pillar), 11, 13, 14 (close to door), 16 (close to pillar) and (narrow space between tables and wall). The quality of the resulting grid map is as good as the one of the frontier based process. The polygonal maps however contain some defects: the stairway area at the east end of the corridor is only visible in the ceiling map (blue) because it is blocked with a halfsize wall at the floor level. This leads to a significant difference between floor and ceiling ground plan, i. e. a very interesting BV. But after BV 8 and 9 the robot records this area as inaccessible and never returns to it. This shows the importance of recording and regarding past BVs that could not be reached in the evaluation function 1. ext, in the kitchen there are missing parts in both polygonal maps because at BV 14 and 16 no panorama scan and subsequent 3D reconstruction was possible. imilarly the north half of the meeting room is missing in the floor map since BV 12 is the only one with a panorama scan in this room. W E

6 In sum the polygonal maps are as good as one can expect from the layout of derived BVs. Concerning calculation of BVs a fallback to frontier evaluation was only needed when a panorama scan was impossible. This shows two things: ground plan based exploration works even in corridors with similar floor and ceiling areas; a backup strategy is needed since 3D reconstruction is not always possible in highly cluttered areas where the robot cannot acquire a reasonable 3D point cloud. Apart from that the resulting number and dispersion of BVs is significant better than the one of pure frontier based exploration, especially in the north and west part of the corridor (without the unnecessary return to its east end at BV 14, 15 this statement would hold for the whole hallway). The smaller number of BVs (25 vs. 36) and panorama scans (12 vs. 25) is also the reason for the smaller duration of the whole process (26 min less). The significant longer travel path (70 m resp. factor 1.5) can be explained by particularities of the path planner: at the beginning the robot drives two times into the north part of the corridor (towards BV 10) for reaching BV 1 because the sparse grid map leads to a suboptimal navigation path. During motion the grid map is improved continuously so that path planning soon becomes more reliable. However this does not shorten the exploration process since the polygonal maps are only updated at BVs (i. e. BV 10 cannot be omitted). In the previous test run the initial BVs are more adjacent so the defects of path planning do not get obvious. At least the longer travel path does not increase duration of exploration because most time is spent at panorama scans. Consequently a reasonable selection and dispersion of BVs by ground plan based exploration makes the whole process more efficient compared to the frontier based approach. 4.3 Hybrid Ground Plan Based Exploration of a Real Office cenario ow that the benefits of hybrid ground plan based exploration have been shown in simulation the proposed strategy has to stand two test runs in an uncontrolled real world office scenario. The first run has been conducted out of office hours, that means without dynamic changes of the environment (no doors opened or closed randomly, no people walking through the scene). Figure 5 shows the resulting grid map, polygonal maps (blue = ceiling, orange = floor), BVs and travel path. It took 56 minutes and 184 m and 28 BVs (8 without panorama scan). This key data is similar to the simulation test run shown in figure 4 (see table 1). This time the run has been started in the south part of the hallway to show that the start position has no influence on the process. The robot first explores the west and north part of the hallway at BV The main failure occurs in front of the left door to the meeting room with an accumulation of BV 3, 5, 9, 11, 13. The robot does not manage to pass this door because of the narrow space in the meeting room close to this passage (door leaf, tables, chairs) which is a more challenging situation than in the simulated scene. Besides small localization errors change the robot pose in the map continuously and let the obstacle memory outdate quickly. ince this collection is not a consecutive sequence of BVs it shows a kind of ping-pong pattern: after BV 3 the robot is pushed back into the hallway (BV 4) due to the dispersion criterion in the evaluation function. But the differences between floor and ceiling in the frontal part of the meeting room stay most attractive which leads to BV 5 and 6 close to the door again. This pattern is repeated four times (BV 7/8-9, BV 10-11, BV 12-13, BV 14/15-16). At the east door of the meeting room a similar situation occurs: the robot does not manage to enter the kitchen at BV 17, so it explores the current room (BV 18-20), then the kitchen (BV 21, 22) and finally the east part of the corridor (BV 23-27). As in simulation a fallback to frontier evaluation was necessary only at BVs without panorama scan: 3, 9, 11, 13 close to the west door of the meeting room, 17, 20 close to its east door, 19 close to tables and 22 close to the south door of the kitchen. E 17, 18, 20 W 3, 5, 9, 11, 13 inaccessible polygons Figure 5: Result of hybrid ground plan based exploration in a real office scenario: grid map (red circles), floor map (orange polygons), ceiling map (blue ones), numbered BVs (blue) and navigation path (green). Apart from the problems at door passages the travel path is reasonable: west and north corridor, meeting room, kitchen, east corridor. Especially there is no oscillation between BVs at very distant parts of the working space. Hence this experiment shows that the ground plan strategy is able to calculate an efficient layout of BVs also in real world scenarios. But it also emphasizes the importance of a good LAM and navigation unit since the best collection of BVs is worthless if they cannot be approached reliably. The resulting grid map is almost as complete as in simulation: only the west wall of the meeting room is missing because the robot does not manage to surround the tables and the sensing range is limited when filling the grid map

7 from the 3D obstacle map. The polygonal maps have similar defects as in simulation: the floor map has gaps in the meeting room due to tables and no panorama scan at the west side of the tables; and the stairway area of the corridor is only visible in the ceiling map due to half-size walls at the floor level. Besides some small polygonal artifacts are interesting: south of BV 4 there is a ceiling polygon outside the grid map. This is caused by a window above the closed door that lets the robot look into the inaccessible adjacent room. imilarly at BV 26 the robot scans parts of the ground and gallery outside the building through the glass door at the east end of the corridor. This shows that it is important to mark BVs as unreachable and discard them via the evaluation function 1 in order to avoid exploration deadlocks. a challenge for the path planner and elastic band analyzer (calculation and adaptation of path to BVs) and for the DP-LAM unit (localization with transient occupied grid cells). Furthermore looking into rooms that cannot be entered yields a lot of attractive, but unreachable BV candidates that have to be filtered out reasonably by the scoring function 1. 2, 3, 18, 19, 20, 21 inaccessible polygons 31, , 38 W E 24,25,26,28 23,27 8,9,10 11,12 inaccessible polygons Figure 6: Overlay of ground truth grid map (red, derived in simulation) and real grid map (gray) acquired via the test run of figure 5. Finally figure 6 gives a hint about the quality of the grid map (gray) recorded in reality compared to the ground truth map (red) as output of the simulation experiment. Here each pixel represents an occupied grid cell of10 10 cm 2. Whereas the orientation of walls is quite good (only orthogonal resp. parallel walls) the grid map recorded in reality is significantly bigger than the ground truth map: the error is about 0.5 m from north to south and up to 1 m from west to east. This error is caused by localization inaccuracies during map recording. This means the applied DP-LAM approach assures a reliable but not perfect localization: closing the loop corridor - meeting room - kitchen works even though the robot does not drive a complete loop (no connection at BV 0-23 in fig. 5). The last test run that shall be analyzed has been performed in the same real office environment as before, but now at office hours. That means people were walking casually through the scene and doors to offices were standing open or being opened/closed randomly. However the layout of the accessible working space has been arranged as in the previous experiments by partially blocking open doors with chairs so that humans, but not the robot could pass. Basically these dynamic changes of the environment are Figure 7: Result of hybrid ground plan based exploration in a real office scenario with dynamic changes: grid map (red circles), floor map (orange polygons), ceiling map (blue ones), numbered BVs (blue) and navigation path (green). Figure 7 shows the resulting grid map, polygonal maps, BVs and travel path after 39 exploration steps. Actually the algorithm stopped after 43 BVs due to no more valid candidates, but the last four BVs are all accumulated between east wall and tables of the meeting room were the robot is stuck at the end of exploration. The process has been started in the entrance hall with the robot facing to the east as in the simulation experiments. First the entrance area is explored (BV 0-2), then the robot enters the meeting room (BV 3). Due to narrow space in this room (expensive navigation path) and similar attractive candidates in the hallway the robot continues exploring the south and east part of the corridor (BV 4-12). At BV 13 it looks into the kitchen but prefers to examine the north part of the hallway (BV 14-16) first. At least here no time consuming double door passing as at BV 3 happens. Afterwards the west part of the hallway is completed (BV 17), then the meeting room is entered again (BV 18-21). As in the previous test run (fig. 5) the robot does not manage to completely enter this room due to narrow space and LAM offsets. Therefore it returns to BV 13 (i. e. BV 22) in front of the kitchen as this is the remaining alternative open end of the map. Moving into the kitchen works smoothly and this room is explored with BV At least BV are obsolete which gives a hint that

8 the robot again has problems to leave the kitchen towards the meeting room. Finally at BV 30 it manages to enter and explores the east part of this room with BV The remaining BVs represent oscillations in this narrow passage because the robot does not manage to surround the tables completely. At least the algorithm detects that there is no valid accessible BV candidate any more and stops. In sum the dispersion and order of BVs: entrance area - south/east corridor - north corridor - kitchen - meeting room is reasonable, especially taking the challenging environmental conditions into account. Exploration defects arise from failure of passing the doors into the meeting room due to the present obstacle situation (tables, chair, pillar close to door). This only delays the exploration process, but does not make it fail. Thus the proposed strategy can be regarded as valid for real world applications. The total time of exploration was 112 min with 278 m length of travel path. This is about one hour and 100 m more than in the previous run (without environmental changes). o panorama scan was possible at BV 3 (door to meeting room), 9, 10 (stairway area), 13 (door to kitchen), (door to meeting room), 23, (kitchen), 30-36, (area between tables and east wall of meeting room). Only at these positions a fallback to frontier evaluation was necessary. o the number of panorama scans is almost the same as in the static environment. This shows that the changing obstacle situation mainly affects path planning and navigation: reaching the actual target pose is not always successful which yields more suboptimal BVs, i. e. positions close to obstacles where a 3D scan is not possible. The grid map covers roughly the same area as in the previous run so only the west wall of the meeting room is missing. Here also the main defect of the floor map occurs since there it is covered by tables and no panorama scan was possible in this area. Besides the corner with the stairway only appears in the ceiling map for known reasons. The only defect of both maps occurs at the north end of the hallway (BV 16) where the 3D reconstruction did not work properly. Remarkable is the collection of artifacts in the polygonal maps (floor and/or ceiling) that arise from scanned areas outside the accessible working space, either due to casually open doors, windows above closed doors or glass doors. These polygons are marked with arrows in figure 7. They yield BV candidates with high values of expected information gain. Yet they have to be discarded since they are unreachable. This shows once more that the scoring function 1 has been designed reasonably. 5 Conclusion and Outlook This paper has introduced a novel hybrid exploration strategy that combines ground plan based exploration with a dynamic extension of the frontier based approach as fallback in obstructed areas. ensor system, LAM and navigation unit have been sketched as basics of an integrated explorer. This one has been implemented as finite state automaton. Its structure and operating principles have been introduced. imulation experiments have shown the benefits of the proposed approach compared to pure frontier based exploration. Furthermore it has been validated as reliable and efficient in a real office scenario even in situations where dynamic obstacles are present. Current work focuses of reducing the restriction of polygonal map updates only at BVs. The goal is to perform quasi online updates also of this map while traveling in order to exploit new environmental information immediately. 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