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Mobile Robot Localization and Map Building
Sadeep Jayasumana University of Oxford Email confirmado em robots. Frederik Schaffalitzky University of Oxford Email confirmado em schaffalitzky. Ver tudo. Email confirmado em anu. Computer Vision optimization forensic imaging. Artigos Citado por Coautores. International workshop on vision algorithms, , The first brands are the simplest, but also those with more limitations when capturing environments with arbitrary shapes.
They are intended to represent structured based on points and lines environments. Each mark is represented by a position and uncertainty associated probability. The second the grid model environment using cells with probability associated occupation.
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Third patterns can be considered an extension of the first, in the sense that seek to expand the restricted set of points and lines with those working in order to take advantage of the information the way of any obstacle to establishing more robust locations not too structured environments. To avoid the limitations of the marks and exploit the features of the other methods in simplified form, the present invention proposes to replace the two-dimensional grid patterns and by entities called objects that consist of a sequence of adjustable moving points dynamically in size with to represent the shape of the contours of the real obstacles to be detected.
Each point represents the sequence that the objects has associated a position and a weight that indicates the degree of mobility thereof. In the case of the invention, each weight associated with an object point represents the probability that its position is located more or less close than it actually represents.
The advantages of this approach are several model map. Thus, in relation to the marks based strategy allows for modeling any accurate and detailed outline without substantial loss of the memory form used. Regarding models based on grid, although both strategies can capture information contours in any way, in our case the memory required to store information of objects grows linearly with the number and length and not quadratically according to the surface map explored so far. In relation-based models, you can incrementally update the shape and size thereof and also allows for a local consistency probability for each point of the object, rather than a single probability associated to complete pattern.
In addition, the map objects can be adapted to local deformations of the contours of the obstacles that may appear in real time. Instead, employers are not averaged to reduce or eliminate uncertainty. Another advantage worth noting is that the grid-based method does not provide information regarding the connectivity of the cells are filled FIG. However, the present invention stores information of objects as entities independent of each other, thus establishing association strategies based on the relative positions of objects.
These objects, therefore, are defined as an ordered sequence of points with position information X, Y and a weight P proportional to the degree of uncertainty in that position. The management of the points helps introduce concepts of computational geometry, as occlusion and shape analysis of objects. Thus, according to the statement on the state of the art, maps based on shape marks only points, lines or patterns defined as the set of sensor measurements. The overall consistency of the map is achieved by maintaining a covariance matrix reflecting uncertainties in the positions of the state vector used, that is, vehicle state position X, Y and orientation and position of other brands.
Journal of Physical Agents
Map based on grid map achieves consistency maintaining a probability map for cell. The map can be modified by varying the probability associated with their cells. However, object-based maps using the weights associated with each point of them as a measure of the degree of overall consistency. When a point is added to an object, its associated weight is the minimum possible for example, 1. As its position is updated over time based on the measurements received from the sensor, its weight increases, so that it is proportional to the number of updates its position using the sensor information FIG.
One of the fundamental differences observed between the map objects and the grid is the first contour stores information classified naturally by physical objects detected by the sensor.
In cases of grid and objects, exists in the first no information about the connectivity of the cells, while the second simply appears naturally. As we illustrated in FIG. This form of internally represent the physical reality provided by the sensor enables us introduce concepts of computational geometry occlusion type between objects and shape analysis objects to see if they are closed on themselves or to associate, based on shape characteristics of the sensor with existing objects.
How to store the sensor information on the objects allows us to associate segmentation sensor related groups of sensor points that refer to the same physical object and call "features" with the map information. In the present invention, the association process is the use of the concept of object to associate robustly map features using geometric criteria shape and occlusion. Robot pose estimation in 2D unknown environments by matching range scans.
In Proc. IEEE Comp. Basically, this method tries to find the relative displacement between two points collections: one from an extracted characteristic of the sensor and the other, of an entity stored in the map. ICP basic idea is to match both collections points using a transformation based on displacement and rotation. For this, a priori associations between points of both sequences are set and the resulting transformation is solved.
The process is repeated until a collection of points fits with the other. Unfortunately, one of the main disadvantages of this algorithm is its convergence. The technique ICP does not converge to the desired result when collections points are very far this occurs when the accumulated vehicle position error is large and a region is revisited: closed loop or when the relative rotation of both is remarkable.
At this point, the present invention solves the problem by using a transformation for a relative rotational displacement between the feature points and the object as described below. To do this, use the information on the form of collections is fit points. This information is summarized in another data stream called signature which is formed by calculating the relative angle between two consecutive points of the original collection of points.
The advantage of working with the document is that it is invariant to translations and rotations of the original sequence. Unlike the ICP algorithm, where you have to build partnerships between the points of the original collections, the proposal presented is to establish these relationships using only the shape information contained in the document where the relevant information as corners and generally , breakpoints, is reflected clearly.
Thus they may be established association hypothesis between points both signatures that correspond to transformations of translation and rotation. Throughout the description and claims the word "comprises" and its variants are not intended to exclude other technical features, additives, components or steps.
To those skilled in the art, other objects, advantages and features of the invention will emerge partly from the description and partly from the practice of the invention. The following examples and drawings are provided by way of illustration and are not intended to restrict the present invention. Furthermore, the present invention covers all possible combinations of particular and preferred embodiments set forth herein. It then goes on to describe very briefly a series of drawings which aid in better understanding the invention and which are expressly related to an embodiment of said invention presented as a non-limiting example thereof.
As indicated, the main contributions of the invention are detailed in the mapping process, the association process and localization process. To carefully explain the invention, first, the association process based on the signature characteristics dimensional and objects based on sequences form is described. Subsequently explained, the localization process based on associative hypothesis among notable points of the symbols of the characteristics and their associated object to achieve a transformation in displacement and rotation that achieves good alignment of the points of the characteristics and objects.
Finally it will be described how the points are moved, deleted or added to the map objects. Objects are defined as an ordered sequence of points. Each contains position information on absolute reference and a probability measure associated with the position information of the point is in the near vicinity of the actual position it represents. The association between observations features from the sensor and the map objects is done trying to fit the signature of observation SOBV in a region of the signature of the object SOBJ. To do this, we calculate the mean squared error of two symbols, so that for each displacement of the symbol on the observation of the object a similarity measure is obtained between both:.
The minimum mean square error function indicates the relative displacement between the symbols for which there is a better fit between the two. Figure 1 shows both signatures and the relative displacement for which the best fit occurs. The association between the regions are segmented features and the objects of the map is performed in parallel by calculating the mean squared error of each of the symbols of the observations SOBV with all the signatures of map objects SOBJ that they are compatible with their position.
The point of the mean square error function occurs where a minimum generate a hypothesis of association between a feature and an object map. The general scheme of association previously seen generates a hypothesis of association, that is, feature couples - object map, where each of these, in turn, establishes a hypothesis localization is resolved as follows. The relative displacement for a minimum function mean squared error RMSE SOBJ, SOBV i allows to establish an association between the symbol points of observation with its associated object occurs; in Figure 3, identical to Figure 1, it shows how the relative displacement occurs for which the minimum is eleven points.
It is represented in Figure 3 a point of a characteristic signature of observation 3 and more specifically the maximum value. The homologous point 3 ' on the signature of the object is represented considering the relative displacement. For construction of the symbol, from the pair of points set forth in Figure 4 another pair of homologous reference points is calculated in the original sequences of feature and object 31, 31 ', 32, 32', 33, 33 ' , 34, 34 ', 35, 35'.
A shift and rotation transformation in two dimensions is defined by at least two pairs of points. One pair is determined by the homologous points calculated above and the other may be set considering as those associated points of the original sequences of their respective equidistant reference points.
Once established associations points in an environment of reference points in both sequences, transformation and rotation movement is determined. In Figure 5 location scheme proposed based on the shape shown. As shown in said figure 5, the hypothesis location 51 is established by the following steps: i. III Establishing the correlation between the symbols of object and feature 54,55 according to:. A step of relative displacement of signatures and establishment of homologous points in the signatures A step of establishing homologous points in the feature and its associated object 57,57 '.
A step of establishing associations between the points of the sequence of the characteristic and the object in a centered homologues landmarks 58 environment. A final step of resolution of the location Once the location of the vehicle, the map is updated from its current state and the information from the sensor.
Updating map is done locally, given that the map objects are defined by a sequence of one-dimensional points. Points defining objects are characterized by their absolute position on the map X, Y and by its weight P , as shown in Figure 6. Updating a map object requires vehicle location calculated at the current time X t and the state map in the previous moment Mu. Thus, the local information of the extracted sensor observations are converted to absolute coordinates of the map using the vehicle location and estimated X t allowing integrated into a single absolute reference sensor data and points map objects.
When updating a region of an object using sensory information, we can distinguish several cases depending on the degree of overlap of the sensor points with the points of the object from the sensor position:. In this case, updating a region of an object it is made by nesting updated points of observation with the object. Updating an observation point it is made by considering the local information of the weights of the two closest points of the object, as seen in Figure 8.
The update point observation is performed as follows:. Where X 0bV t are updated absolute coordinates of the observation point; X 0bV n are the absolute coordinates without updating the point of observation; X 0bV They are the absolute coordinates of the intersection point between the observation beam connecting the sensor with the observation point without updating X 0b v ti and the segment defined by the two closest map points to the latter. CASE 2 There are observation points that do not overlap with the associated object points. In this case the update object region is performed by adding the observation points that do not overlap the object.
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New items are introduced into the object are weight 1. This, as shown in Figure 9. CASE 3 points exist an object does not overlap with any extracted observation points sensor. As shown in Figure 10, the update object area not overlapping with an observation is performed by subtracting one unit weights points that region. When the weight of the points is negative, the object to which it belongs is eliminated. Practical embodiment of the invention to take full advantage of the innovations described a possible practical realization hardware thereof is presented using an FPGA.
This is characterized in that a programmable logic device general purpose. FPGAs are composed of configurable logic blocks BLC that communicate with each other through programmable connections. Thus, any FPGA includes a two dimensional array of these blocks surrounded modifiable connections between them. Furthermore, in order to communicate with the outside FPGA, it has a set of input ports and user-configurable output. Then, following the recommended by the present invention, a hardware implemented in an FPGA embodiment described.
Since the most remarkable feature of a FPGA is to allow running in different parallel tasks in Figure 1 1 other blocks for executing the invention properly is. Thus each block represents the reserve of the configurable logic resources of the global FPGA to achieve a particular purpose.
The system comprises a first segmentation module representing all resources of the FPGA configured to perform segmentation are input sensor signal St in order to generate features. Similarly, in the system of the invention the prediction module , the association module and location , the fusion module and the generator module object map implemented. Each of the above modules is implemented by logic blocks, which together allow to define the functionality of each module within the FPGA programmed into the system. Thus we have the following basic configurable logic blocks: a configurable logic block are actual point type 10 configured to represent information associated with a point of the sensor are range and angle.
Thus, from a location of the vehicle the spread of rays on the map objects, just as happens with the rays of the sensor on real objects in the environment is simulated. Therefore, this block attempt related information X, Y position of the particle as well as its weight. Therefore, the information handled is a position X, Y and an orientation. The segmentation process implemented in the segmentation module is configured to extract the characteristics of the sensor are. The criterion for deciding that a region of points are rises to range usually feature local proximity between two consecutive points of that region.
If two of them are relatively close, it is assumed that belong to the same feature. Otherwise, a feature ends and another begins. Taking advantage of the parallel processing power of the FPGA, the segmentation module comprises many configurable logic blocks are actual point type 10 containing the measures as are; all configurable logic blocks are connected one after another, so that except the ends logical blocks, each have two neighbors, as shown in Figure From the initial information range and angle a point are loaded on each logical block are true 10 , each of these carried out in parallel the comparison of its range with its left neighbor for example , storing the difference in the block itself.
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Next, a sequential scan of the logic blocks from right to left comparing the difference in ranges of a block stored in the block of the left is performed. If the comparison does not exceed a certain threshold which depends on the application , both points belong to the same characteristic; otherwise, a counter number detected characteristics is increased and continues the comparison between neighbors.
Furthermore, the generator module object map reserves the necessary logic blocks particle type configurable logic block like particle 30 to store information of each of the map objects.
Each map object comprises a set of these configurable logic blocks like particle 30 connected together. Similarly, removal of a point of an object results in deletion of the corresponding block associated with point. The localization and association module is configured to establish the association between the extracted features are the objects and the map. As has been stated above, it is based on cross-correlation of two signatures. The mean square error two-dimensional sequence is given by the expression: This equation requires a large number of multiplications, subtractions and additions.
In a general-purpose microprocessor, the calculation of this function implies a high computational cost, because it can not run in parallel. However, in an FPGA, you can be performed in hardware in an efficient manner using ad hoc hardware performing additions and multiplications simultaneously. Most FPGA contain some logic blocks that perform these types of operations multiply accumulate atomically. Normally, such operations are called MAC multiple accumulation or multiplyaccumulation operations.
Herein called MAC 50 blocks. Hardware mean square error between the symbol of an object and a feature shown in Figure As seen in this figure, the necessary hardware comprises a shift register 51 or delay line this is a memory array that shifts the contents of each position one place to the right or left in each clock cycle and many MAC 50 blocks as elements having the signature of the object that we set the mean square error.
The operation is described below. First data object signature are loaded into a memory array. In the example shown in the figure, the data line B. Then the data of the characteristic signature is loaded into a delay line. In each clock cycle, the delay line shifted one memory location to the right the content of each position.