Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

In this paper we examine the problem of autonomously exploring and mapping an environment using a mobile robot. The robot uses a graph-based SLAM system to perform mapping and represents the map as an occupancy grid. In this setting, the robot must trade-off between exploring new area to complete the task and exploiting the existing information to maintain good localization. Selecting actions that decrease the map uncertainty while not significantly increasing the robot's localization uncertainty is challenging. We present a novel information-theoretic utility function that uses both Shannon's and Rényi's definitions of entropy to jointly consider the uncertainty of the robot and the map. This allows us to fuse both uncertainties without the use of manual tuning. We present simulations and experiments comparing the proposed utility function to state-of-the-art utility functions, which only use Shannon's entropy. We show that by using the proposed utility function, the robot and map uncertainties are smaller than using other existing methods.

In this paper we investigate the monotonicity of various optimality criteria during the exploration phase of an active SLAM algorithm. Optimality criteria such as A-opt, D-opt or E-opt are used in active SLAM to account for uncertainty in the map or the robot's pose, and these criteria are usually part of utility functions which help active SLAM algorithms decide where the robot should move next. The monotonicity of the optimality criteria is of utmost importance. During the exploration phase, i.e. when the robot is traversing new territory or cannot perform a loop closure, the most common way of estimating the pose of the robot is through dead-reckoning. Correctly accounting for the uncertainty is important for an active SLAM algorithm and in particular for a dead-reckoning scenario, where by definition the uncertainty in the robot's pose grows. If monotonicity does not hold in this scenario, active SLAM algorithms can execute actions under the false belief that the uncertainty has reduced. We show analytically and experimentally some conditions in which the A-opt and E-opt criteria lose monotonicity in a dead-reckoning scenario, where the propagation of the robot's pose is done using a linearized framework. We also show analytically and experimentally that under the same conditions the D-opt does not lose monotonicity and, in general for the linearized framework under consideration, D-opt does not break monotonicity.

Place categorization addresses the problem of determining the semantic label of the current position of a robot, given a snapshot of the environment as well as previously labeled information about different places that the robot has already seen. State-of-the-art approaches use machine learning techniques that require extensive and often time consuming training. This work proposes a novel formulation by posing place categorization as an efficient ℓ1-minimization problem, leading to both a faster training phase and to performance comparable to state-of-the-art methods. The formulation allows online robot operation particularly in the case when the training phase has to be learned on-the-fly and in an active manner. To validate the performance of the proposed method, extensive experimental results carried out on real data under different lighting conditions as well as structural changes in the environment are provided.

This paper studies different criteria for selecting configurations for the task of calibrating a robotic system. Given an automatic and self-contained procedure which allows the robot to calibrate itself without the need of external tools, we are interested in how to select the set of configurations that maximize calibration accuracy while minimizing calibration time. We experiment with the active calibration of a multi-sensorial humanoid's upper body and report that determinant-based criteria should be preferred when a greedy selection is used. In addition to criteria comparison, we further propose a new criterion for configuration selection. Its novelty stems from a direct treatment of the robot's end-effector tool variance. This is contrary to previous approaches which target the variance indirectly via calibration parameters. Our proposed objective function is derived as a compact formulation from the mean error of the robot's end-effector tool from which its variance can be computed using traditional criteria known from the theory of optimal experimental design (e.g. A-optimality).

In this paper, we consider the computation of the D-optimality criterion as a metric for the uncertainty of a SLAM system. Properties regarding the use of this uncertainty criterion in the active SLAM context are highlighted, and comparisons against the A-optimality criterion and entropy are presented. This paper shows that contrary to what has been previously reported, the D-optimality criterion is indeed capable of giving fruitful information as a metric for the uncertainty of a robot performing SLAM. Finally, through various experiments with simulated and real robots, we support our claims and show that the use of D-opt has desirable effects in various SLAM related tasks such as active mapping and exploration.

This paper addresses the problem of path planning considering uncertainty criteria over the belief space. Specifically, we propose a path planning algorithm that uses a novel determinant-based measure of uncertainty and a reduced representation of the environment, in order to obtain the minimum uncertainty path from a roadmap. Our proposal does not require a priori knowledge of the environment due to the construction of the roadmap via a graph-based SLAM algorithm. We report experimental results of our proposal in four datasets that show its feasibility to obtain the minimum uncertainty path towards an autonomous navigation framework and we also show an improvement in the computation time with respect to the state of the art.

Sperm morphology has a key role in the determination of fertility in men. Nowadays assessments of sperm morphology are mostly done with subjective criteria. This article discusses details of the stages implemented in a computer aided tool for the objective analysis of human sperm morphology, commonly known as Automated Sperm Morphology Analyzer (ASMA). The first stage described is the detection and extraction of individual spermatozoon of an image containing multiple spermatozoa and biological rests as well. A novel method for segmenting objects in microscopic images into its constituent's parts is proposed. The method called nth-fusion introduced in this paper is the framework of the segmentation algorithm implemented in this computer aided tool, this algorithm has been tested with a 250 spermatozoon images database and the results are quite accurate. The final stage, feature extraction and classification, estimates the morphological parameters of human spermatozoon and classifies them according to the World Healthcare Organization criteria.