nils-herrmann/clean_scirepeval huggingface dataset:
Map building with mobile robots in dynamic environments

The problem of generating maps with mobile robots has received considerable attention over the past years. Most of the techniques developed so far have been designed for situations in which the environment is static during the mapping process. Dynamic objects, however, can lead to serious errors in the resulting maps such as spurious objects or misalignments due to localization errors. In this paper we consider the problem of creating maps with mobile robots in dynamic environments. We present a new approach that interleaves mapping and localization with a probabilistic technique to identify spurious measurements. In several experiments we demonstrate that our algorithm generates accurate 2D and 3D in different kinds of dynamic indoor and outdoor environments. We also use our algorithm to isolate the dynamic objects and generate 3D representation of them.
Real time data association for FastSLAM

The ability to simultaneously localise a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. This paper presents a real-world implementation of FastSLAM, an algorithm that recursively estimates the full posterior distribution of both robot pose and landmark locations. In particular, we present an extension to FastSLAM that addresses the data association problem using a nearest neighbor technique. Building on this, we also present a novel multiple hypotheses tracking implementation (MHT) to handle uncertainty in the data association. Finally an extension to the multi-robot case is introduced. Our algorithm has been run successfully using a number of data sets obtained in outdoor environments. Experimental results are presented that demonstrate the performance of the algorithms when compared with standard Kalman filter-based approaches.
ARA*: Anytime A* with Provable Bounds on Sub-Optimality

In real world planning problems, time for deliberation is often limited. Anytime planners are well suited for these problems: they find a feasible solution quickly and then continually work on improving it until time runs out. In this paper we propose an anytime heuristic search, ARA*, which tunes its performance bound based on available search time. It starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows. Given enough time it finds a provably optimal solution. While improving its bound, ARA* reuses previous search efforts and, as a result, is significantly more efficient than other anytime search methods. In addition to our theoretical analysis, we demonstrate the practical utility of ARA* with experiments on a simulated robot kinematic arm and a dynamic path planning problem for an outdoor rover.
A system for volumetric robotic mapping of abandoned mines

This paper describes two robotic systems developed for acquiring accurate volumetric maps of underground mines. One system is based on a cart instrumented by laser range finders, pushed through a mine by people. Another is a remotely controlled mobile robot equipped with laser range finders. To build consistent maps of large mines with many cycles, we describe an algorithm for estimating global correspondences and aligning robot paths. This algorithm enables us to recover consistent maps several hundreds of meters in diameter, without odometric information. We report results obtained in two mines, a research mine in Bruceton, PA, and an abandoned coal mine in Burgettstown, PA.
Decentralized Sensor Fusion with Distributed Particle Filters

This paper presents a scalable Bayesian technique for decentralized state estimation from multiple platforms in dynamic environments. As has long been recognized, centralized architectures impose severe scaling limitations for distributed systems due to the enormous communication overheads. We propose a strictly decentralized approach in which only nearby platforms exchange information. They do so through an interactive communication protocol aimed at maximizing information flow. Our approach is evaluated in the context of a distributed surveillance scenario that arises in a robotic system for playing the game of laser tag. Our results, both from simulation and using physical robots, illustrate an unprecedented scaling capability to large teams of vehicles.
An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements

The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems [J.-S. Gutman et al., 1999]. This paper presents a novel algorithm that combines Rao-Blackwellized particle filtering and scan matching. In our approach scan matching is used for minimizing odometric errors during mapping. A probabilistic model of the residual errors of scan matching process is then used for the resampling steps. This way the number of samples required is seriously reduced. Simultaneously we reduce the particle depletion problem that typically prevents the robot from closing large loops. We present extensive experiments that illustrate the superior performance of our approach compared to previous approaches.
A robotic walker that provides guidance

This paper describes a robotic walker designed as an assistive device for frail elderly people with cognitive impairment. Locomotion is most often the primary form of exercise for the elderly, and devices that provide mobility assistance are critical for the health and well being of such individuals. Previous work on walkers focused primarily on safety but offered little or no assistance with navigation and global orientation. Our system provides these features in addition to the stability and support provided by conventional walkers. A software suite of robot localization and navigation combined with a shared-control haptic interface achieves this capability. The system has been tested in a retirement facility near Pittsburgh, PA, USA.
Locating moving entities in indoor environments with teams of mobile robots

This article presents an implemented multi-robot system for playing the popular game of laser tag. The object of the game is to search for and tag opponents that can move freely about the environment. The main contribution of this paper is a new particle filter algorithm for tracking the location of many opponents in the presence of pervasive occlusion. We achieve efficient tracking principally through a clever factorization of our posterior into roles that can be dynamically added and merged. When searching for opponents, the individual agents greedily maximize their information gain, using a negotiation technique for coordinating their search efforts. Experimental results are provided, obtained with a physical robot system in large-scale indoor environments and through simulation.
Towards Lazy Data Association in SLAM

We present a lazy data association algorithm for the simultaneous localization and mapping (SLAM) problem. Our approach uses a tree-structured Bayesian representation of map posteriors that makes it possible to revise data association decisions arbitrarily far into the past. We describe a criterion for detecting and repairing poor data association decisions. This technique makes it possible to acquire maps of large-scale environments with many loops, with a minimum of computational overhead for the management of multiple data association hypotheses. A empirical comparison with the popular FastSLAM algorithm shows the advantage of lazy over proactive data association.
Multi-robot SLAM with Sparse Extended Information Filers

We present an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem. Our algorithm enables teams of robots to build joint maps, even if their relative starting locations are unknown and landmarks are ambiguous—which is presently an open problem in robotics. It achieves this capability through a sparse information filter technique, which represents maps and robot poses by Gaussian Markov random fields. The alignment of local maps into a single global maps is achieved by a tree-based algorithm for searching similar-looking local landmark configurations, paired with a hill climbing algorithm that maximizes the overall likelihood by search in the space of correspondences. We report favorable results obtained with a real-world benchmark data set.
Decentralised SLAM with Low-Bandwidth Communication for Teams of Vehicles

This paper addresses the problem of simultaneous localization and mapping (SLAM) for teams of collaborating vehicles where the communication bandwidth is limited. We present a novel SLAM algorithm that enables multiple vehicles to acquire a joint map, but which can cope with arbitrary latency and bandwidth limitations such as typically found in airborne vehicle applications. The key idea is to represent maps in information form (negative log-likelihood), and to selectively communicate subsets of the information tailored to the available communication resources. We show that our communication scheme preserves the consistency, which has important ramifications for data association problems. We also provide experimental results that illustrate the effectiveness of our approach in comparison with previous
Gaussian Multi-Robot SLAM

We present an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem. Our algorithm enables teams of robots to build joint maps, even if their relative starting locations are unknown and landmarks are ambiguous— which is presently an open problem in robotics. It achieves this capability through a sparse information filter technique, which represents maps and robot poses by Gaussian Markov random fields. The alignment of local maps into a single global maps is achieved by a tree-based algorithm for searching similar-looking local landmark configurations, paired with a hill climbing algorithm that maximizes the overall likelihood by search in the space of correspondences. We report favorable results obtained with a real-world benchmark data set. Submitted to NIPS*2003
1 Towards Lazy Data Association in SLAM

We present a lazy data association algorithm for the simultaneous localization and mapping (SLAM) problem. Our approach uses a tree-structured Bayesian representation of map posteriors that makes it possible to revise data association decisions arbitrarily far into the past. We describe a criterion for detecting and repairing poor data association decisions. This technique makes it possible to acquire maps of large-scale environments with many loops, with a minimum of computational overhead for the management of multiple data association hypotheses. A empirical comparison with the popular FastSLAM algorithm shows the advantage of lazy over proactive data association.
A Monadic Probabilistic Language

Motivated by many practical applications that have to compute in the presence of uncertainty, we propose a monadic probabilistic language based upon the mathematical notion of sampling function. Our language provides a unified representation scheme for probability distributions, enjoys rich expressiveness, and offers high versatility in encoding probability distributions. We also develop a novel style of operational semantics called a horizontal operational semantics, under which an evaluation returns not a single outcome but multiple outcomes. We have preliminary evidence that the horizontal operational semantics improves the ordinary operational semantics with respect to both execution time and accuracy in representing probability distributions.
An Approximate Bayesian Method for Simultaneous Localisation and Mapping

This paper describes a Bayesian formulation of the Simultaneous Localisation and Mapping (SLAM) problem. Previously, the SLAM problem could only be solved in real time through the use of the Kalman Filter. This generally restricts the application of SLAM methods to domains with straight-forward (analytic) environment and sensor models. In this paper the Sum-of-Gaussian (SOG) method is used to approximate more general (arbitrary) probability distributions. This representation permits the generalizations made possible by Monte-Carlo methods, while inheriting the real-time computational advantages of the Kalman filter. The method is demonstrated by its application to sub-sea field data. The sub-sea data consists of both sonar and visual information of near-field landmarks. This is a particularly challenging problem incorporating diverse sensing modalities, amorphous environment features, and poorly known vehicle dynamics; none of which can be easily handled by Kalman filter-based SLAM algorithms.
High-level robot behavior control using POMDPs

This paper describes a robot controller which uses probabilistic decision-making techniques at the highest-level of behavior control. The POMDP-based robot controller has the ability to incorporate noisy and partial sensor information, and can arbitrate between information gathering and performance-related actions. The complexity of the robot control domain requires a POMDP model that is beyond the capability of current exact POMDP solvers, therefore we present a hierarchical variant of the POMDP model which exploits structure in the problem domain to accelerate planning. This POMDP controller is implemented and tested onboard a mobile robot in the context of an interactive service task. During the course of experiments conducted in an assisted living facility, the robot successfully demonstrated that it could autonomously provide guidance and information to elderly residents with mild physical and cognitive disabilities.
Conditional particle filters for simultaneous mobile robot localization and people-tracking

Presents a probabilistic algorithm for simultaneously estimating the pose of a mobile robot and the positions of nearby people in a previously mapped environment. This approach, called the conditional particle filter, tracks a large distribution of person locations conditioned upon a smaller distribution of robot poses over time. This method is robust to sensor noise, occlusion, and uncertainty in robot localization. In fact, conditional particle filters can accurately track people in situations with global uncertainty over robot pose. The number of samples required by this filter scales linearly with the number of people being tracked, making the algorithm feasible to implement in real-time in environments with large numbers of people. Experimental results illustrate the accuracy of tracking and model selection, as well as the performance of an active following behavior based on this algorithm.
A System for Volumetric Robotic Mapping of Underground Mines

We describe two robotic systems for acquiring high-resolution volumetric maps of underground mines. Our systems have been deployed in an operational coal mine in Bruceton, Pennsylvania, where they have been used to generate interactive 3-D maps. Our approach includes a novel sensor head, assembled from multiple SICK laser range finders, and a real-time algorithm for scan matching that generates accurate volumetric maps. The scan matching algorithm performs horizontal and vertical simultaneous localization and mapping (SLAM). Data from the horizontal scans is used to remove artifacts in the vertical scans, and vice versa. The system can construct full 3-D volumetric maps hundreds of meters in diameter, even when no odometry information is available.
An integrated approach to hierarchy and abstraction for pomdps

This paper presents an algorithm for planning in structured partially observable Markov Decision Processes (POMDPs). The new algorithm, named PolCA (for Pol icy-Contingent Abstraction) uses an action-based decomposition to partition complex POMDP problems into a hierarchy of smaller subproblems. Low-level subtasks are solved rst, and their partial policies are used to model abstract actions in the context of higher-level subtasks. At all levels of the hierarchy, subtasks need only consider a reduced action, state and observation space. The reduced action set is provided by a designer, whereas the reduced state and observations sets are discovered automatically on a subtask-per-subtask basis. This typically results in lower-level subtasks having few, but high-resolution, state/observations features, whereas high-level subtasks tend to have many, but low-resolution, state/observation features. This paper presents a detailed overview of PolCA in the context of a POMDP hierarchical planning and execution algorithm. It also includes theoretical results demonstrating that in the special case of fully observable MDPs, the algorithm converges to a recursively optimal solution. Experimental results included in the paper demonstrate the usefulness of the approach on a range of problems, and show favorable performance compared to competing function-approximation POMDP algorithms. Finally, the paper presents a real-world implementation and deployment of a robotic system which uses PolCA in the context of a high-level robot behavior control task.
Towards object mapping in non-stationary environments with mobile robots

We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects change their locations over time. Our approach uses a straightforward map differencing technique to detect changes in an environment over time. It employs the expectation maximization algorithm to learn models of non-stationary objects, and to determine the location of such objects in individual occupancy grid maps built at different points in time. By combining data from multiple maps when learning object models, the resulting models have higher fidelity than could be obtained from any single map. A Bayesian complexity measure is applied to determine the number of different objects in the model, making it possible to apply the approach to situations where not all objects are present at all times in the map.
Learning motion patterns of persons for mobile service robots

We propose a method for learning models of people's motion behaviors in an indoor environment. As people move through their environments, they do not move randomly. Instead, they often engage in typical motion patterns, related to specific locations that they might be interested in approaching and specific trajectories that they might follow in doing so. Knowledge about such patterns may enable a mobile robot to develop improved people following and obstacle avoidance skills. This paper proposes an algorithm that learns collections of typical trajectories that characterize a person's motion patterns. Data, recorded by mobile robots equipped with laser range finders, is clustered into different types of motion using the popular expectation maximization algorithm, while simultaneously learning multiple motion patterns. Experimental results, obtained using data collected in a domestic residence and in an office building, illustrate that highly predictive models of human motion patterns can be learned.
Using EM to learn motion behaviors of persons with mobile robots

We propose a method for learning models of people's motion behaviors in indoor environments. As people move through their environments, they do not move randomly. Instead, they often engage in typical motion patterns, related to specific locations that they might be interested in approaching and specific trajectories that they might follow in doing so. Knowledge about such patterns may enable a mobile robot to develop improved people following and obstacle avoidance skills. This paper proposes an algorithm that learns collections of typical trajectories that characterize a person's motion patterns. Data, recorded by mobile robots equipped with laser-range finders, is clustered into different types of motion using the popular expectation maximization algorithm, while simultaneously learning multiple motion patterns. Experimental results, obtained using data collected in a domestic residence and in an office building, illustrate that highly predictive models of human motion patterns can be learned.
Real-time acquisition of compact volumetric 3D maps with mobile robots

Describes an online algorithm for generating compact 3D maps of mobile robot environments. Maps generated by our approach consist of small numbers of planar surfaces, which are augmented by fine-grained polygons for non-flat environmental features. Our approach builds on the expectation maximization (EM) algorithm, but develops a new, incremental version that can be executed in real-time. Experimental results obtained in corridor-type environments illustrate that compact and accurate maps can be acquired in real-time, from range and camera data collected by a mobile robot.
A System for Three-Dimensional Robotic Mapping of Underground Mines

The authors describe two robotic systems for acquiring high-resolution volumetric maps of underground mines. Their systems have been deployed in an operational coal mine in Bruceton, Pennsylvania, where they have been used to generate interactive 3-dimensional maps. Their approach includes a novel sensor head assembled from multiple SICK laser range finders, and a real-time algorithm for scan matching that generates accurate volumetric maps. The scan matching algorithm performs horizontal and vertical simultaneous localization and mapping (SLAM). Data from the horizontal scans is used to remove artifacts in the vertical scans, and vice versa. The system can construct full 3-dimensional volumetric maps hundreds of meters in diameter, even when no odometry information is available.
Learning Occupancy Grids of Non-Stationary Objects with Mobile Robots

We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects may change their locations over time. Most mapping algorithms rely on a static world assumption, which cannot model non-stationary objects (chairs, desks,...). This paper describes an extension to the well-known occupancy grid mapping technique [5,10] for learning models of non-stationary objects. Our approach uses a map differencing technique to extract snapshots of non-stationary objects. It then employs the expectation maximization (EM) algorithm to learn models of these objects, and to solve the data association problem that arises when objects are seen at different places at different points in time. A Bayesian version of Occam’s razor is applied to determine the number of different objects in the model. Experimental results obtained in two different indoor environments illustrate that our approach robustly solves the data association problem, and generates accurate models of environments with non-stationary objects.
Concurrent mapping and localization for mobile robots with segmented local maps

A map of the environment map is a model of the surroundings of the robot. In many robot application domains, it is infeasible to obtain a prior map of the environment. It is therefore desirable that robots are able to generate maps by themselves. This problem is challenging, since it involves a simultaneous localization problem of the robot relative to its (incomplete) map. This article presents a new probabilistic algorithm for the simultaneous localization and mapping problem. The algorithm is based on matching of a set of local maps that have been obtained from range data. The main advantage of the proposed method is of computational nature. The use of segmented maps permits to eliminate unnecessary details, leading the method to be very appropriate for dynamical environments.