18 accepted papers
Robotic systems demand accurate and comprehensive 3D environment perception, requiring simultaneous capture of photo-realistic appearance (optical), precise layout shape (geometric), and open-vocabulary scene understanding (semantic). Existing methods typically achieve only partial fulfillment of th…
In daily domestic settings, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this cha…
Outdoor loop closure detection is essential for mitigating accumulated drift in SLAM and generating a global consistent map. Semantic graph matching methods utilize object-level topology for distinctive scene representation but rely on environments with rich and distinguishable objects. Moreover, ac…
Implicit reconstruction of ESDF (Euclidean Signed Distance Field) involves training a neural network to regress the signed distance from any point to the nearest obstacle, which has the advantages of lightweight storage and continuous querying. However, existing algorithms usually rely on conflictin
In daily domestic settings, frequently used objects like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cups</i> often have unfixed positions and multiple instances within the same category, and their carriers also frequently change. As a result, it
In everyday life, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, th
Loop closure detection and pose estimation play a significant role in correcting odometry trajectories and generating globally consistent point cloud maps. Geometric feature descriptor methods neglect object-level spatial topology features, resulting in inadequate performance in loop closure detecti
Collaborative mapping aids agents in achieving an efficient and comprehensive understanding of their environment. Recently, there has been growing interest in using neural networks as maps to represent functions that implicitly define the geometric features of a scene. However, existing implicit map
This letter explores the problem of delivering unwieldy objects using nonholonomic mobile bases. We propose a new approach called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">free pushing</i> to address this challenge. Unlike previous <italic xmln
Two-point zeroth order methods are important in many applications of zeroth-order optimization arising in robotics, wind farms, power systems, online optimization, and adversarial robustness to black-box attacks in deep neural networks, where the problem can be high-dimensional and/or time-varying.…
Filtering out outlier data associations between local maps can improve the robustness and accuracy of multi-robot localization. When the overlap is low and the field of view difference is large, it is likely to produce outlier data associations between local maps, which will reduce the matching accu…
Capturing the semantics of objects and the topological relationship allows the robot to describe the scene more intelligently like a human and measure the similarity between scenes (loop closure detection) more accurately. However, many current semantic graph matching methods are based on walk descr…
This letter addresses the problem of pushing manipulation with nonholonomic mobile robots. Pushing is a fundamental skill that enables robots to move unwieldy objects that cannot be grasped. We propose a stable pushing method that maintains stiff contact between the robot and the object to avoid con
Single-point zeroth-order optimization (SZO) is useful in solving online black-box optimization and control problems in time-varying environments, as it queries the function value only once at each time step. However, the vanilla SZO method is known to suffer from a large estimation variance and slo…
Inertial measurement units are widely used in different fields to estimate the attitude. Many algorithms have been proposed to improve estimation performance. However, most of them still suffer from 1) inaccurate initial estimation, 2) inaccurate initial filter gain, and 3) non-Gaussian process and/…
This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. Lyapunov’s method in control theory is employed to prove the convergence of orientation estimation errors. The estimator gains and a Lyapunov function are…
This paper focuses on the problem of multi-robot source-seeking, where a group of mobile sensors localizes and moves close to a single source using only local measurements. Drawing inspiration from the optimal sensor placement research, we develop an algorithm that estimates the source location whil…
Auto-exploration is a task for self-driving robots to explore unknown environments, which becomes much complicated when they move on irregular outdoor terrains. To improve the situation, a new frontier-based exploration algorithm is presented in this paper. It starts from original 3D cloud points of…