Tootfinder

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms
Mateusz Wasala, Mateusz Smolarczyk, Michal Danilowicz, Tomasz Kryjak
https://arxiv.org/abs/2507.07903

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms
Accurate position estimation is essential for modern navigation systems deployed in autonomous platforms, including ground vehicles, marine vessels, and aerial drones. In this context, Visual Simultaneous Localisation and Mapping (VSLAM) - which includes Visual Odometry - relies heavily on the reliable extraction of salient feature points from the visual input data. In this work, we propose an embedded implementation of an unsupervised architecture capable of detecting and describing feature po…

Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
Taku Okawara, Kenji Koide, Aoki Takanose, Shuji Oishi, Masashi Yokozuka, Kentaro Uno, Kazuya Yoshida
https://arxiv.org/abs/2506.09548

Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
In this letter, we present tightly coupled LiDAR-IMU-leg odometry, which is robust to challenging conditions such as featureless environments and deformable terrains. We developed an online learning-based leg kinematics model named the neural leg kinematics model, which incorporates tactile information (foot reaction force) to implicitly express the nonlinear dynamics between robot feet and the ground. Online training of this model enhances its adaptability to weight load changes of a robot (e.…

Deep Visual Odometry for Stereo Event Cameras
Sheng Zhong, Junkai Niu, Yi Zhou
https://arxiv.org/abs/2509.08235 https://arxiv.org/pdf/2509.08235

Deep Visual Odometry for Stereo Event Cameras
Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle state estimation tasks involving motion blur and high dynamic range (HDR) illumination conditions. However, the versatility of event-based visual odometry (VO) relying on handcrafted data association (either direct or indirect methods) is still unreliable, especially in field robot applications under low-light HD…

SVN-ICP: Uncertainty Estimation of ICP-based LiDAR Odometry using Stein Variational Newton
Shiping Ma, Haoming Zhang, Marc Toussaint
https://arxiv.org/abs/2509.08069 https://

SVN-ICP: Uncertainty Estimation of ICP-based LiDAR Odometry using Stein Variational Newton
This letter introduces SVN-ICP, a novel Iterative Closest Point (ICP) algorithm with uncertainty estimation that leverages Stein Variational Newton (SVN) on manifold. Designed specifically for fusing LiDAR odometry in multisensor systems, the proposed method ensures accurate pose estimation and consistent noise parameter inference, even in LiDAR-degraded environments. By approximating the posterior distribution using particles within the Stein Variational Inference framework, SVN-ICP eliminates…

AEOS: Active Environment-aware Optimal Scanning Control for UAV LiDAR-Inertial Odometry in Complex Scenes
Jianping Li, Xinhang Xu, Zhongyuan Liu, Shenghai Yuan, Muqing Cao, Lihua Xie
https://arxiv.org/abs/2509.09141

AEOS: Active Environment-aware Optimal Scanning Control for UAV LiDAR-Inertial Odometry in Complex Scenes
LiDAR-based 3D perception and localization on unmanned aerial vehicles (UAVs) are fundamentally limited by the narrow field of view (FoV) of compact LiDAR sensors and the payload constraints that preclude multi-sensor configurations. Traditional motorized scanning systems with fixed-speed rotations lack scene awareness and task-level adaptability, leading to degraded odometry and mapping performance in complex, occluded environments. Inspired by the active sensing behavior of owls, we propose A…

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Sai Puneeth Reddy Gottam, Haoming Zhang, Eivydas Keras
https://arxiv.org/abs/2509.08333

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Visual-based localization has made significant progress, yet its performance often drops in large-scale, outdoor, and long-term settings due to factors like lighting changes, dynamic scenes, and low-texture areas. These challenges degrade feature extraction and tracking, which are critical for accurate motion estimation. While learning-based methods such as SuperPoint and SuperGlue show improved feature coverage and robustness, they still face generalization issues with out-of-distribution data…

A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling
Meher V. R. Malladi, Tiziano Guadagnino, Luca Lobefaro, Cyrill Stachniss
https://arxiv.org/abs/2509.06593

A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling
Accurate odometry is a critical component in a robotic navigation stack, and subsequent modules such as planning and control often rely on an estimate of the robot's motion. Sensor-based odometry approaches should be robust across sensor types and deployable in different target domains, from solid-state LiDARs mounted on cars in urban-driving scenarios to spinning LiDARs on handheld packages used in unstructured natural environments. In this paper, we propose a robust LiDAR-inertial odometry sy…

Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy
Liansheng Wang, Xinke Zhang, Chenhui Li, Dongjiao He, Yihan Pan, Jianjun Yi
https://arxiv.org/abs/2509.05723

Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy
LiDAR-Inertial Odometry (LIO) is a foundational technique for autonomous systems, yet its deployment on resource-constrained platforms remains challenging due to computational and memory limitations. We propose Super-LIO, a robust LIO system that demands both high performance and accuracy, ideal for applications such as aerial robots and mobile autonomous systems. At the core of Super-LIO is a compact octo-voxel-based map structure, termed OctVox, that limits each voxel to eight fused subvoxels…

SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation
Juyeop Han, Lukas Lao Beyer, Guilherme V. Cavalheiro, Sertac Karaman
https://arxiv.org/abs/2507.07467

SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation
Autonomous flight in GPS denied indoor spaces requires trajectories that keep visual localization error tightly bounded across varied missions. Whereas visual inertial odometry (VIO) accumulates drift over time, scene coordinate regression (SCR) yields drift-free, high accuracy absolute pose estimation. We present a perception-aware framework that couples an evidential learning-based SCR pose estimator with a receding horizon trajectory optimizer. The optimizer steers the onboard camera toward …

VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots
Miguel \'A. Gonz\'alez-Santamarta, Francisco J. Rodr\'iguez-Lera, Vicente Matell\'an-Olivera
https://arxiv.org/abs/2506.09583

VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots
Localization plays a crucial role in the navigation capabilities of autonomous robots, and while indoor environments can rely on wheel odometry and 2D LiDAR-based mapping, outdoor settings such as agriculture and forestry, present unique challenges that necessitate real-time localization and consistent mapping. Addressing this need, this paper introduces the VAULT prototype, a ROS 2-based mobile mapping system (MMS) that combines various sensors to enable robust outdoor and indoor localization.…