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Similarly, McCormac et al. Moreover, one of the participating teams from benchmarked a pose estimation method on a warehouse logistics dataset, and found large variations in performance depending on clutter level and object type [ 2 ]. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality.

A task scheduling mechanism dictates when the robot should visit which waypoints, depending on the tasks the robot has to accomplish on any given day. However, the danger is to overfit to such benchmarks, as the deployment environment of mobile robots is almost sure to differ from the one used in teaching the robot to perceive and understand the surrounding environment. This can involve, for instance, observing an object of interest from multiple viewpoints in order to allow a better object model estimation, or even in-hand modeling.

The aim of the work presented by Brucker et al. Subsequently, a motion and a grasp are computed and executed.


Building maps of the environment is a crucial part of any robotic system and arguably one of the most researched areas in robotics. Therefore, different assumptions can be incorporated in the mapping representation and perception systems considering indoor or outdoor environments. Usually, in traditional mobile robot manipulation use-cases, the and manipulation capabilities of a robot can be exploited to let the robot gather data about objects autonomously.

Vision-based perception systems typically suffer from occlusions, aspect ratio influence, and from problems arising due to the discretization of the 3D or 6D search space. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics.

Nowadays, most of robotic perception systems use machine learning ML techniques, ranging from classical to deep-learning approaches [ 17 ]. The target could be a table or container where something has to be put down, or an object to be picked up.

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Research in self-driving cars, also referred as autonomous robot-cars, is closely related to mobile robotics and many important works in this field have been published in well-known conferences and journals devoted to robotics. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The approaches covered range from metric representations 2D or 3D to higher semantic or topological maps, and all serve specific purposes key to the successful operation of a mobile robot, such as localization,object detection, manipulation, etc.

As presented by Hermans et al. Online methods process data as it is being acquired by the mobile robot, and generate a semantic map incrementally. Unfortunately, at the moment an off-the-shelf DL solution for every problem does not exist, or at least no usable pretrained network, making the need for huge amounts of training data apparent. Applications of Mobile Robots. Data alignment and calibration steps are necessary depending on the nature of the problem and the type of sensors used.

In this regard, perception and action reciprocally inform each other, in order to obtain the best for locating objects. Once a robot is self localized, it can proceed with the execution of its task. Processing sensory data and storing it in a representation of the environment i.

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Machine learning for robotic perception can be in the form of unsupervised learning, or supervised classifiers using handcrafted features, or deep-learning neural networks e. Thus, the suggestions formulated by Wagstaff [ 19 ] still hold true today and should be taken to heart by researchers and practitioners.

Data can come from a single or multiple sensors, usually mounted onboard the robot, but can also come from the infrastructure or from another robot e. Essentially, a robot is deed to operate in two of environments: indoors or outdoors. While deep learning holds the potential to both improve accuracy i.

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Conversely, in the works of [ 464748 ], they predict the object pose through voting or a PnP algorithm [ 49 ]. Regardless of the ML approach considered, data from sensor s are the key ingredient in robotic perception. Domain adaptation and domain randomization i. Moreover, the sensors used are different depending on the environment, and therefore, the sensory data to be processed by a perception system will not be the same for indoors and outdoors scenarios.


Especially in the latter case, estimating all 6 degrees of freedom of an object is necessary. Figure 3 shows a high level overview of the Strands system with more details in [ 52 ] : the mobile robot navigates autonomously between a of predefined waypoints.

The problem of object pose estimation, an important prerequisite for model-based robotic grasping, uses in most of the cases precomputed grasp points as described by Ferrari and Canny [ 41 ]. The recent work presented by Brucker et al. Strands aims to fill this gap and to provide robots that are intelligent, robust, and can provide useful functions in real-world security and care scenarios.

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Robotic perception is crucial for a robot to make decisions, plan, and operate in real-world environments, by means of numerous functionalities and operations from occupancy grid mapping to object detection. Similarly, in the work proposed by Mura et al. Also, Brucker et al. Since strong AI is still far from being achieved in real-world robotics applications, this chapter is about weak AIi. The method selection often boils down to obtaining the latest pretrained network from an online repository and fine-tuning it to the problem at hand, hiding all the traditional feature detection, description, filtering, matching, optimization steps behind a relatively unified framework.

In the methods described by Ambrus et al. The performance usually decreases if the considered object lacks texture and if the background is heavily cluttered. Importantly, the extended operation times imply that the robotic systems developed have to be able to cope with an ever-increasing amount of data, as well as to be able to deal with the complex and unstructured real world Figure 2.

The rationale is to improve robustness and safety by providing complementary information to the perception system, for example: the position and identification of a given object or obstacle on the road could be reported e.

The development of advanced perception for full autonomous driving has been a subject of interest since the s, having a period of strong development due to the DARPA Challenges, and and the European ELROB challenges sinceand more recently, it has regained considerable interest from automotive and robotics industries and academia. In addition, considerable effort has been made in the semantic labeling of these maps, at pixel and voxels levels. The EU FP7 Strands project [ 52 ] is formed by a consortium of six universities and two industrial partners.

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In multiple-sensors perception, either the same modality or multimodal, an efficient approach is usually necessary to combine and process data from the sensors before an ML method can be employed. Early work coupled mapping with localization as part of the simultaneous localization and mapping SLAM problem [ 2223 ].

In addition to the sensors e. In a typical setup, the robot navigates to the region of interest, observes the current scene to build a 3D map for collision-free grasp planning and for localizing target objects. There are cases where a tighter integration of perception and manipulation is required, e. These methods are usually coupled with a SLAM framework, which ensures the geometric consistency of the map. Among the numerous approaches used in environment representation for mobile robotics, and for autonomous robotic-vehicles, the most influential approach is the occupancy grid mapping [ 20 ].

Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the of the notorious evolution and success of ML algorithms. However, the main limitation in [ 34 ] is that the approach requires knowledge of the positions from which the environment was scanned when the input data were collected. Some examples of robotic perception subareas, including autonomous robot-vehicles, are obstacle detection [ 23 ], object recognition [ 45 ], semantic place classification [ 67 ], 3D environment representation [ 8 ], gesture and voice recognition [ 9 ], activity classification [ 10 ], terrain classification [ 11 ], road detection [ 12 ], vehicle detection [ 13 ], pedestrian detection [ 14 ], object tracking [ 3 ], human detection [ 15 ], and environment change detection [ 16 ].

An example to clarify the differences and challenges between a mobile robot navigating in an indoor versus outdoor environment is the ground, or terrain, where the robot operates. The key components of a perception system are essentially sensory data processing, data representation environment modelingand ML-based algorithms, as illustrated in Figure 1.

In robotics, perception is understood as a system that endows the robot with the ability to perceive, comprehend, and reason about the surrounding environment. The perception system consists, at the lowest level, of a module which builds local metric maps at the waypoints visited by the robot. Therefore, perception is a very important part of a complex, embodied, active, and goal-driven robotic system.

This semantic mapping process uses ML at various levels, e. Moreover, in outdoors, robotic perception has to deal with weather conditions and variations in light intensities and spectra. The approaches presented in [ 434445 ] make use of color histograms, color gradients, depth or normal orientations from discrete object views, i. While research into mobile robotic technology has been very active over the last few decades, robotic systems that can operate robustly, for extended periods of time, in human-populated environments remain a rarity.

In the case of autonomous mobile manipulators, this involves localizing the objects of interest in the operating environment and grasping them. In the works listed above, learning algorithms based on classical ML methods and deep-learning e.

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In this context, the localization problem involves segmenting objects, but also knowing their position and orientation relative to the robot in order to facilitate manipulation. Robot perception functions, like localization andare dependent on the environment where the robot operates. These local maps are updated over time, as the robot revisits the same locations in the environment, and they are further used to segment out the dynamic objects from the static scene.

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However, in the majority of applications, the primary role of environment mapping is to model data from exteroceptive sensors, mounted onboard the robot, in order to enable reasoning and inference regarding the real-world environment where the robot operates. However, in every application, there is a potential improvement for treating perception and manipulation together. Although many approaches use 2D-based representations to model the real world, presently 2. Perception and manipulation are complementary ways to understand and interact with the environment and according to the common coding theory, as developed and presented by Sperry [ 35 ], they are also inextricably linked in the brain.

In the case of perception for mobile robots and autonomous robot vehicles, such options are not available; thus, its perception systems have to be trained offline. Most of indoor robots assume that the ground is regular and flat which, in some manner, facilitates the environment representation models; on the other hand, for field outdoors robots, the terrain is quite often far from being regular and, as consequence, the environment modeling is itself a challenge and, without a proper representation, the subsequent perception tasks are negatively affected.

In both cases, an ever-recurring approach is that bottom-up data-driven hypothesis generation is followed and verified by top-down concept-driven models. Such mechanisms are assumed, as addressed by Frisby and Stone [ 42 ], to be like our human vision system. The argument for embodied learning and grounding of new information evolved, considering the works of Steels and Brooks [ 38 ] and Vernon [ 39 ], and more recently in [ 40 ], robot perception involves planning and interactive segmentation. This 2D mapping is still used in many mobile platforms due to its efficiency, probabilistic framework, and fast implementation.

Most of the relevant approaches can be split into two main trends: methods deed for online and those deed for offline use. However, current solutions are either heavily tailored to a specific application, requiring specific engineering during deployment, or their generality makes them too slow or imprecise to fulfill the tight time-constraints of industrial applications.

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The advent and proliferation of RGBD sensors has enabled the construction of larger and ever-more detailed 3D maps. The aim of the project is to develop the next generation of intelligent mobile robots, capable of operating alongside humans for extended periods of time. A of semantic mapping approaches are deed to operate offline, taking as input a complete map of the environment.

Thus, perception systems currently require expert knowledge in order to select, adapt, extend, and fine-tune the various employed components. Apart from the increased training data sizes and robustness, the end-to-end training aspect of deep-learning DL approaches made the development of perception systems easier and more accessible for newcomers, as one can obtain the desired directly from raw data in many cases, by providing a large of training examples.

The main reasons for using higher dimensional representations are essentially twofold: 1 robots are demanded to navigate and make decisions in higher complex environments where 2D representations are insufficient; 2 current 3D sensor technologies are affordable and reliable, and therefore 3D environment representations became attainable.

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Moreover, the ability to construct a geometrically accurate map further annotated with semantic information also can be used in other applications such as building management or architecture, or can be further fed back into a robotic system, increasing the awareness of its surroundings and thus improving its ability to perform certain tasks in human-populated environments e. More recent work has focused on dealing with or incorporating time-dependencies short or long term into the underlying structure, using either grid maps as described in [ 824 ], pose-graph representations in [ 25 ], and normal distribution transform NDT [ 1626 ].

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