JournalTOCs

Autonomous Robots
Journal Prestige (SJR): 1.131

Citation Impact (citeScore): 3
Number of Followers: 11

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-7527 - ISSN (Online) 0929-5593
Published by Springer-Verlag

[2468 journals]

Autonomous learning-free grasping and robot-to-robot handover of unknown
objects
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  Abstract: In this paper, we propose a learning-free approach for an autonomous robotic system to grasp, hand over, and regrasp previously unseen objects. The proposed framework includes two main components: a novel grasping detector to predict grasping poses directly from the point cloud and a reachability-aware handover planner to select the exchange pose and grasping poses for two robots. In the grasping detection stage, multiple superquadrics are first recovered at different positions within the object, representing the local geometric feature of the object. Our algorithm then exploits the tri-symmetry feature of superquadrics and synthesizes a list of antipodal grasps from each recovered superquadric. An evaluation model is designed to assess and quantify the quality of each grasp candidate. In the handover planning stage, the planner first selects grasping candidates that have high scores and a larger number of collision-free partners. Then the exchange location is computed by utilizing two signed distance fields (SDF) which model the reachability space for the pair of two robots. To evaluate the performance of the proposed method, we first run experiments on isolated and packed scenes to corroborate the effectiveness of our grasping detection method. Then the handover experiments are conducted on a dual-arm system with two 7 degrees of freedom (DoF) manipulators. The results indicate that our method shows better performance compared with the state-of-the-art, without the need for large amounts of training.
  PubDate: 2025-06-28
Multi-robot exploration for the CADRE mission
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  Abstract: We present the design, implementation and testing of a multi-robot exploration algorithm for NASA’s upcoming Cooperative Autonomous Distributed Robotic Exploration (CADRE) lunar technology demonstration mission. The CADRE mission, among its various objectives, entails utilizing a trio of autonomous mobile robots to collaboratively explore and construct a map of a designated area of the lunar surface. Given the mission’s inherent constraints, including limited mission duration, constrained power resources, and restricted communication capabilities, we formulate an exploration algorithm to improve exploration efficiency, facilitate equitable workload distribution among individual agents, and minimize inter-robot communication. To achieve these requirements, we employ a semi-centralized exploration algorithm that partitions the unexplored area, regardless of its shape and size, into a series of non-overlapping partitions, assigning each partition to a specific robot for exploration. Each robot autonomously explores its designated region without intervention from other robots. We explore the design space of the proposed algorithm and evaluate its performance under diverse conditions in simulations. Finally, we validate the algorithm’s functionality through two sets of hardware experiments: the first utilizes prototype rovers using a ROS-based navigation software stack for feasibility testing, while the second employs high-fidelity development model rovers running CADRE’s custom flight-software stack for flight-like performance validation. Both sets of experiments are conducted in the Jet Propulsion Laboratory’s lunar-simulated rover testing facilities, demonstrating the algorithm’s robustness and readiness for lunar deployment.
  PubDate: 2025-06-12
Effective tracking of unknown clustered targets using a distributed team
of mobile robots
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  Abstract: Distributed multi-target tracking is a canonical task for multi-robot systems, encompassing applications from environmental monitoring to disaster response to surveillance. In many situations the unknown distribution of the targets in a search area is non-uniform, e.g., herds of animals moving together. This paper develops a novel distributed multi-robot multi-target tracking algorithm to effectively search for and track clustered targets. There are two key features. First, there are two parallel estimators, one to provide the best guess of the current states of targets and a second to provide a coarse, long-term distribution of clusters. Second, robots use the power diagram to divide the search space between agents in a way that effectively trades off between tracking detected targets within high density areas and searching for other potential targets. Extensive simulation experiments demonstrate the efficacy of the proposed method and show that it outperforms other approaches in tracking accuracy of clustered targets while maintain good performance for uniformly distributed targets.
  PubDate: 2025-05-24
LSF-planner: a visual local planner for legged robots based on ground
structure and feature information
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  Abstract: Three-dimensional navigation of legged robots is crucial for field exploration and post-disaster rescue. Existing optimization-based local trajectory planners predominantly focus on obstacle avoidance, neglecting negative obstacles (e.g., pits) and varying ground features (e.g., different terrain types). Additionally, non-overlapping areas between the planned space in three-dimensional trajectory planning and the robot’s actual reachable space lead to decision-making issues between crossing and obstacle avoidance, making it challenging to differentiate between passable and hazardous areas, thus impacting navigation safety and stability. To address these limitations, we propose a novel visual local planner, LSF-Planner (Visual Local Planner for Legged Robots Based on Ground Structure and Feature Information). The LSF-Planner employs a multi-layer local perception map that integrates ground feature semantics, sensor range, and negative obstacles (e.g., voids detected by depth sensors) to construct a ground reliability representation. The Label2Grad method is introduced to convert this representation into gradient layers, incorporating a ground reliability penalty function into trajectory optimization. By incorporating constraints on the center of mass height and crossing angles, LSF-Planner effectively differentiates between traversable and hazardous areas. Experimental results show that LSF-Planner significantly outperforms existing methods in 3D trajectory planning, enhancing the navigation performance of legged robots in unstructured environments.
  PubDate: 2025-05-12
Shortest coordinated motions for square robots
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  Abstract: We study the problem of determining minimum-length coordinated motions for two axis-aligned square robots translating in an obstacle-free plane: Given feasible start and goal configurations (feasible in the sense that the two squares are interior disjoint), find a continuous motion for the two squares from start to goal, comprising only robot-robot collision-free configurations, such that the total Euclidean distance traveled by the two squares is minimal among all possible such motions. In this paper we present an adaptation of the tools developed for the case of disks to the case of squares. We show that in certain aspects the case of squares is more complicated, requiring additional and more involved arguments over the case of disks.
  PubDate: 2025-05-08
FIMD: fast isolated marker detection for UV-based visual relative
localisation in agile UAV swarms
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  Abstract: A novel approach for the fast onboard detection of isolated markers for visual relative localisation of multiple teammates in agile UAV swarms is introduced in this paper. As the detection forms a key component of real-time localisation systems, a three-fold innovation is presented, consisting of an optimised procedure for CPUs, a GPU shader program, and a functionally equivalent FPGA streaming architecture. For the proposed CPU and GPU solutions, the mean processing time per pixel of input camera frames was accelerated by two to three orders of magnitude compared to the unoptimised state-of-the-art approach. For the localisation task, the proposed FPGA architecture offered the most significant overall acceleration by minimising the total delay from camera exposure to detection results. Additionally, the proposed solutions were evaluated on various 32-bit and 64-bit embedded platforms to demonstrate their efficiency, as well as their feasibility for applications using low-end UAVs and MAVs. Thus, it has become a crucial enabling technology for agile UAV swarming.
  PubDate: 2025-05-06
Deadlock-free, safe, and decentralized multi-robot navigation in social
mini-games via discrete-time control barrier functions
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  Abstract: We present an approach to ensure safe and deadlock-free navigation for decentralized multi-robot systems operating in constrained environments, including doorways and intersections. Although many solutions have been proposed that ensure safety and resolve deadlocks, optimally preventing deadlocks in a minimally invasive and decentralized fashion remains an open problem. We first formalize the objective as a non-cooperative, non-communicative, partially observable multi-robot navigation problem in constrained spaces with multiple conflicting agents, which we term as social mini-games. Formally, we solve a discrete-time optimal receding horizon control problem leveraging control barrier functions for safe long-horizon planning. Our approach to ensuring liveness rests on the insight that there exists barrier certificates that allow each robot to preemptively perturb their state in a minimally-invasive fashion onto liveness sets i.e. states where robots are deadlock-free. We evaluate our approach in simulation as well on physical robots using F1/10 robots, a Clearpath Jackal, as well as a Boston Dynamics Spot in a doorway, hallway, and corridor intersection scenario. Compared to both fully decentralized and centralized approaches with and without deadlock resolution capabilities, we demonstrate that our approach results in safer, more efficient, and smoother navigation, based on a comprehensive set of metrics including success rate, collision rate, stop time, change in velocity, path deviation, time-to-goal, and flow rate.
  PubDate: 2025-04-20
Fault-tolerant multi-robot localization: diagnostic decision-making with
information theory and learning models
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  Abstract: In the domain of multi-robot systems, cooperative systems that are highly attuned and connected to their surroundings are becoming increasingly significant. This surge in interest highlights various challenges, especially regarding system integration and safety constraints. Our research contributes to the assurance of fault tolerance to avert abnormal behaviors and sustain reliable robot localization. In this paper, a mixed approach between data-driven and model-based for fault detection is introduced, within a decentralized architecture, thereby strengthening the system’s capacity to handle simultaneous sensor faults. Information theory-based fault indicators are developed by computing the Jensen-Shannon divergence ($$D_{JS}$$) between state predictions and sensor-obtained corrections. This initiates a two-tiered data-driven mechanism: one layer employing Machine Learning for fault detection, and another distinct layer for fault isolation. The methodology’s efficacy is assessed using real data from the Turtlebot3 platform.
  PubDate: 2025-04-17
Human2bot: learning zero-shot reward functions for robotic manipulation
from human demonstrations
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  Abstract: Developing effective reward functions is crucial for robot learning, as they guide behavior and facilitate adaptation to human-like tasks. We present Human2Bot (H2B), advancing the learning of such a generalized multi-task reward function that can be used zero-shot to execute unknown tasks in unseen environments. H2B is a newly designed task similarity estimation model that is trained on a large dataset of human videos. The model determines whether two videos from different environments represent the same task. At test time, the model serves as a reward function, evaluating how closely a robot’s execution matches the human demonstration. While previous approaches necessitate robot-specific data to learn reward functions or policies, our method can learn without any robot datasets. To achieve generalization in robotic environments, we incorporate a domain augmentation process that generates synthetic videos with varied visual appearances resembling simulation environments, alongside a multi-scale inter-frame attention mechanism that aligns human and robot task understanding. Finally, H2B is integrated with Visual Model Predictive Control (VMPC) to perform manipulation tasks in simulation and on the xARM6 robot in real-world settings. Our approach outperforms previous methods in simulated and real-world environments trained solely on human data, eliminating the need for privileged robot datasets.
  PubDate: 2025-04-15
Between reality and delusion: challenges of applying large language models
to companion robots for open-domain dialogues with older adults
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  Abstract: Throughout our lives, we interact daily in conversations with our friends and family, covering a wide range of topics, known as open-domain dialogue. As we age, these interactions may diminish due to changes in social and personal relationships, leading to loneliness in older adults. Conversational companion robots can alleviate this issue by providing daily social support. Large language models (LLMs) offer flexibility for enabling open-domain dialogue in these robots. However, LLMs are typically trained and evaluated on textual data, while robots introduce additional complexity through multi-modal interactions, which has not been explored in prior studies. Moreover, it is crucial to involve older adults in the development of robots to ensure alignment with their needs and expectations. Correspondingly, using iterative participatory design approaches, this paper exposes the challenges of integrating LLMs into conversational robots, deriving from 34 Swedish-speaking older adults’ (one-to-one) interactions with a personalized companion robot, built on Furhat robot with GPT$$-$$3.5. These challenges encompass disruptions in conversations, including frequent interruptions, slow, repetitive, superficial, incoherent, and disengaging responses, language barriers, hallucinations, and outdated information, leading to frustration, confusion, and worry among older adults. Drawing on insights from these challenges, we offer recommendations to enhance the integration of LLMs into conversational robots, encompassing both general suggestions and those tailored to companion robots for older adults.
  PubDate: 2025-03-10
ASAP-MPC: an asynchronous update scheme for online motion planning with
nonlinear model predictive control
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  Abstract: This paper presents a Nonlinear Model Predictive Control (NMPC) update scheme targeted at motion planning for mechatronic motion systems, such as drones and mobile platforms. NMPC-based motion planning typically requires low computation times to be able to provide control inputs at the required rate for system stability, disturbance rejection, and overall performance. To achieve online NMPC updates in complex situations, works in literature typically rely on one of two approaches: attempting to reduce the solution times in NMPC by sacrificing feasibility guarantees, or allowing more time to the motion planning algorithm, which requires additional strategies to ensure robust tracking of the planned motion, e.g., state feedback. Following this second paradigm, this paper presents As-Soon-As-Possible MPC (ASAP-MPC), an asynchronous update scheme for online motion planning with optimal control that abandons the idea of having to satisfy restrictive real-time update rates and that solves the optimal control problem to full convergence. ASAP-MPC combines trajectory generation through optimal control with additional tracking control for improved robustness against disturbances and plant-model mismatch. The scheme seamlessly connects trajectories, resulting from subsequent NMPC solutions, providing a smooth and continuous overall trajectory for the motion system. This framework’s applicability to embedded applications is shown on two different experiment setups where a state-of-the-art method fails to successfully navigate through a given environment: a quadcopter flying through a cluttered environment with hardware-in-the-loop simulation and a scale model truck-trailer manoeuvring in a structured physical lab environment.
  PubDate: 2025-03-07
Isolated Kalman filtering: theory and decoupled estimator design
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  Abstract: In this paper, we propose a state decoupling strategy for Kalman filtering problems, when the dynamics of individual estimates are decoupled and their outputs are sparsely coupled. The algorithm is termed Isolated Kalman Filtering (IsoKF) and exploits the sparsity in the output coupling by applying approximations that mitigate the need for non-involved estimates. We prove that the approximations made during the isolated coupling of estimates are based on an implicit maximum determinant completion of the incomplete a priori covariance matrix. The steady state behavior is studied on eleven different observation graphs and a buffering scheme to support delayed (i.e. out-of-order) measurements is proposed. We discussed handling of delayed measurements in both, an optimal or a suboptimal way. The credibility of the isolated estimates are evaluated on a linear and nonlinear toy example in Monte Carlo simulations. The presented paradigm is made available online to the community within a generic C++ estimation framework supporting both, modular sensor fusion and collaborative state estimation.
  PubDate: 2025-02-13
Eigen-factors a bilevel optimization for plane SLAM of 3D point clouds
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  Abstract: Modern depth sensors can generate a huge number of 3D points in few seconds to be later processed by Localization and Mapping algorithms. Ideally, these algorithms should handle efficiently large sizes of Point Clouds (PC) under the assumption that using more points implies more information available. The Eigen Factors (EF) is a new algorithm that solves PC SLAM by using planes as the main geometric primitive. To do so, EF exhaustively calculates the error of all points at complexity O(1), thanks to the Summation matrix S of homogeneous points. The solution of EF is a bilevel optimization where the lower-level problem estimates the plane variables in closed-form, and the upper-level non-linear problem uses second order optimization to estimate sensor poses (trajectory). We provide a direct analytical solution for the gradient and Hessian based on the homogeneous point-plane constraint. In addition, two variants of the EF are proposed: one pure analytical derivation and a second one approximating the problem to an alternating optimization showing better convergence properties. We evaluate the optimization processes (back-end) of EF and other state-of-the-art plane SLAM algorithms in a synthetic environment, and extended to ICL dataset (RGBD) and LiDAR KITTI datasets. EF demonstrates superior robustness and accuracy of the estimated trajectory and improved map metrics. Code is publicly available at https://github.com/prime-slam/EF-plane-SLAM with python bindings and pip package.
  PubDate: 2025-02-01
View: visual imitation learning with waypoints
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  Abstract: Robots can use visual imitation learning (VIL) to learn manipulation tasks from video demonstrations. However, translating visual observations into actionable robot policies is challenging due to the high-dimensional nature of video data. This challenge is further exacerbated by the morphological differences between humans and robots, especially when the video demonstrations feature humans performing tasks. To address these problems we introduce Visual Imitation lEarning with Waypoints (VIEW), an algorithm that significantly enhances the sample efficiency of human-to-robot VIL. VIEW achieves this efficiency using a multi-pronged approach: extracting a condensed prior trajectory that captures the demonstrator’s intent, employing an agent-agnostic reward function for feedback on the robot’s actions, and utilizing an exploration algorithm that efficiently samples around waypoints in the extracted trajectory. VIEW also segments the human trajectory into grasp and task phases to further accelerate learning efficiency. Through comprehensive simulations and real-world experiments, VIEW demonstrates improved performance compared to current state-of-the-art VIL methods. VIEW enables robots to learn manipulation tasks involving multiple objects from arbitrarily long video demonstrations. Additionally, it can learn standard manipulation tasks such as pushing or moving objects from a single video demonstration in under 30 min, with fewer than 20 real-world rollouts. Code and videos here: https://collab.me.vt.edu/view/
  PubDate: 2025-01-18
Safe and stable teleoperation of quadrotor UAVs under haptic shared
autonomy
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  Abstract: We present a novel approach that aims to address both safety and stability of a haptic teleoperation system within a framework of Haptic Shared Autonomy (HSA). We use Control Barrier Functions (CBFs) to generate the control input that follows the user’s input as closely as possible while guaranteeing safety. In the context of stability of the human-in-the-loop system, we limit the force feedback perceived by the user via a small $$\mathcal {L}_2$$-gain, which is achieved by limiting the control and the force feedback via a differential constraint. Specifically, with the property of HSA, we propose two pathways to design the control and the force feedback: Sequential Control Force (SCF) and Joint Control Force (JCF). Both designs can achieve safety and stability but with different responses to the user’s commands. We conducted experimental simulations to evaluate and investigate the properties of the designed methods. We also tested the proposed method on a physical quadrotor UAV and a haptic interface.
  PubDate: 2025-01-17
Synthesizing compact behavior trees for probabilistic robotics domains
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  Abstract: Complex robotics domains (e.g., remote exploration applications and scenarios involving interactions with humans) require encoding high-level mission specifications that consider uncertainty. Most current fielded systems in practice require humans to manually encode mission specifications in ways that require amounts of time and expertise that can become infeasible and limit mission scope. Therefore, we propose a method of automating the process of encoding mission specifications as behavior trees. In particular, we present an algorithm for synthesizing behavior trees that represent the optimal policy for a user-defined specification of a domain and problem in the Probabilistic Planning Domain Definition Language (PPDDL). Our algorithm provides access to behavior tree advantages including compactness and modularity, while alleviating the need for the time-intensive manual design of behavior trees, which requires substantial expert knowledge. Our method converts the PPDDL specification into solvable MDP matrices, simplifies the solution, i.e. policy, using Boolean algebra simplification, and converts this simplified policy to a compact behavior tree that can be executed by a robot. We present simulated experiments for a marine target search and response scenario and an infant-robot interaction for mobility domain. Our results demonstrate that the synthesized, simplified behavior trees have approximately between 15 x and 26 x fewer nodes and an average of between 8 x and 13 x fewer active conditions for selecting the active action than they would without simplification. These compactness and activity results suggest an increase in the interpretability and execution efficiency of the behavior trees synthesized by the proposed method. Additionally, our results demonstrate that this synthesis method is robust to a variety of user input mistakes, and we empirically confirm that the synthesized behavior trees perform equivalently to the optimal policy that they are constructed to logically represent.
  PubDate: 2025-01-14
Integrative biomechanics of a human–robot carrying task: implications
for future collaborative work
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  Abstract: Patients with sarcopenia, who face difficulties in carrying heavy loads, may benefit from collaborative robotic assistance that is modeled after human–human interaction. The objective of this study is to describe the kinematics and spatio-temporal parameters during a collaborative carrying task involving both human and robotic partners. Fourteen subjects carried a table while moving forward with a human and a robotic partner. The movements were recorded using a three-dimensional motion capture system. The subjects successfully completed the task of carrying the table with the robot. No significant differences were found in the shoulder and elbow flexion/extension angles. In human–human dyads, the center of mass naturally oscillated vertically with an amplitude of approximately 2 cm. The here presented results of the human–human interaction serve as a model for the development of future robotic systems, designed for collaborative manipulation.
  PubDate: 2025-01-09
Mori-zwanzig approach for belief abstraction with application to belief
space planning
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  Abstract: We propose a learning-based method to extract symbolic representations of the belief state and its dynamics in order to solve planning problems in a continuous-state partially observable Markov decision processes (POMDP) problem. While existing approaches typically parameterize the continuous-state POMDP into a finite-dimensional Markovian model, they are unable to preserve fidelity of the abstracted model. To improve accuracy of the abstracted representation, we introduce a memory-dependent abstraction approach to mitigate the modeling error. The first major contribution of this paper is we propose a Neural Network based method to learn the non-Markovian transition model based on the Mori-Zwanzig (M-Z) formalism. Different from existing work in applying M-Z formalism to autonomous time-invariant systems, our approach is the first work generalizing the M-Z formalism to robotics, by addressing the non-Markovian modeling of the belief dynamics that is dependent on historical observations and actions. The second major contribution is we theoretically show that modeling the non-Markovian memory effect in the abstracted belief dynamics improves the modeling accuracy, which is the key benefit of the proposed algorithm. Simulation experiment of a belief space planning problem is provided to validate the performance of the proposed belief abstraction algorithms.
  PubDate: 2024-12-24
Multirotor nonlinear model predictive control based on visual servoing of
evolving features
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  Abstract: This article presents a Visual Servoing Nonlinear Model Predictive Control (NMPC) scheme for autonomously tracking a moving target using multirotor Unmanned Aerial Vehicles (UAVs). The scheme is developed for surveillance and tracking of contour-based areas with evolving features. NMPC is used to manage input and state constraints, while additional barrier functions are incorporated in order to ensure system safety and optimal performance. The proposed control scheme is designed based on the extraction and implementation of the full dynamic model of the features describing the target and the state variables. Real-time simulations and experiments using a quadrotor UAV equipped with a camera demonstrate the effectiveness of the proposed strategy.
  PubDate: 2024-11-28
BFAR: improving radar odometry estimation using a bounded false alarm rate
detector
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  Abstract: This work introduces a novel detector, bounded false-alarm rate (BFAR), for distinguishing true detections from noise in radar data, leading to improved accuracy in radar odometry estimation. Scanning frequency-modulated continuous wave (FMCW) radars can serve as valuable tools for localization and mapping under low visibility conditions. However, they tend to yield a higher level of noise in comparison to the more commonly employed lidars, thereby introducing additional challenges to the detection process. We propose a new radar target detector called BFAR which uses an affine transformation of the estimated noise level compared to the classical constant false-alarm rate (CFAR) detector. This transformation employs learned parameters that minimize the error in odometry estimation. Conceptually, BFAR can be viewed as an optimized blend of CFAR and fixed-level thresholding designed to minimize odometry estimation error. The strength of this approach lies in its simplicity. Only a single parameter needs to be learned from a training dataset when the affine transformation scale parameter is maintained. Compared to ad-hoc detectors, BFAR has the advantage of a specified upper-bound for the false-alarm probability, and better noise handling than CFAR. Repeatability tests show that BFAR yields highly repeatable detections with minimal redundancy. We have conducted simulations to compare the detection and false-alarm probabilities of BFAR with those of three baselines in non-homogeneous noise and varying target sizes. The results show that BFAR outperforms the other detectors. Moreover, We apply BFAR to the use case of radar odometry, and adapt a recent odometry pipeline, replacing its original conservative filtering with BFAR. In this way, we reduce the translation/rotation odometry errors/100 m from 1.3%/0.4$$^\circ $$ to 1.12%/0.38$$^\circ $$, and from 1.62%/0.57$$^\circ $$ to 1.21%/0.32$$^\circ $$, improving translation error by 14.2% and 25% on Oxford and Mulran public data sets, respectively.
  PubDate: 2024-11-19

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