Search Results for author: Maurice Fallon

Found 14 papers, 1 papers with code

Exosense: A Vision-Centric Scene Understanding System For Safe Exoskeleton Navigation

no code implementations21 Mar 2024 Jianeng Wang, Matias Mattamala, Christina Kassab, Lintong Zhang, Maurice Fallon

We demonstrate the system's robustness to the challenges of typical periodic walking gaits, and its ability to construct accurate semantically-rich maps in indoor settings.

Language Modelling Motion Planning +1

SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection

no code implementations11 Mar 2024 Yifu Tao, Yash Bhalgat, Lanke Frank Tarimo Fu, Matias Mattamala, Nived Chebrolu, Maurice Fallon

We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures.

Fast Traversability Estimation for Wild Visual Navigation

no code implementations15 May 2023 Jonas Frey, Matias Mattamala, Nived Chebrolu, Cesar Cadena, Maurice Fallon, Marco Hutter

We demonstrate the advantages of our approach with experiments and ablation studies in challenging environments in forests, parks, and grasslands.

Navigate Self-Supervised Learning +1

Multi-modal curb detection and filtering

no code implementations14 May 2022 Sandipan Das, Navid Mahabadi, Saikat Chatterjee, Maurice Fallon

We propose a robust curb detection and filtering technique based on the fusion of camera semantics and dense lidar point clouds.

Multi-Camera LiDAR Inertial Extension to the Newer College Dataset

no code implementations16 Dec 2021 Lintong Zhang, Marco Camurri, David Wisth, Maurice Fallon

We present a multi-camera LiDAR inertial dataset of 4. 5 km walking distance as an expansion of the Newer College Dataset.

Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry

no code implementations13 Sep 2021 Lintong Zhang, David Wisth, Marco Camurri, Maurice Fallon

We present a multi-camera visual-inertial odometry system based on factor graph optimization which estimates motion by using all cameras simultaneously while retaining a fixed overall feature budget.

feature selection

Online Estimation of Diameter at Breast Height (DBH) of Forest Trees Using a Handheld LiDAR

no code implementations3 Aug 2021 Alexander Proudman, Milad Ramezani, Maurice Fallon

While mobile LiDAR sensors are increasingly used to scan in ecology and forestry applications, reconstruction and characterisation are typically carried out offline (to the best of our knowledge).

VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots

no code implementations15 Jul 2021 David Wisth, Marco Camurri, Maurice Fallon

This bias is observable because of the tight fusion of this preintegrated velocity factor with vision, lidar, and IMU factors.

Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry

no code implementations13 Nov 2020 David Wisth, Marco Camurri, Sandipan Das, Maurice Fallon

True integration of lidar features with standard visual features and IMU is made possible using a subtle passive synchronization of lidar and camera frames.

Landmark Tracking

Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure

no code implementations28 Jan 2020 Milad Ramezani, Georgi Tinchev, Egor Iuganov, Maurice Fallon

The efficiency of our method comes from carefully designing the network architecture to minimize the number of parameters such that this deep learning method can be deployed in real-time using only the CPU of a legged robot, a major contribution of this work.

SKD: Keypoint Detection for Point Clouds using Saliency Estimation

no code implementations10 Dec 2019 Georgi Tinchev, Adrian Penate-Sanchez, Maurice Fallon

We present SKD, a novel keypoint detector that uses saliency to determine the best candidates from a point cloud for tasks such as registration and reconstruction.

Keypoint Detection Saliency Prediction

Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU

no code implementations26 Feb 2019 Georgi Tinchev, Adrian Penate-Sanchez, Maurice Fallon

Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors.

Loop Closure Detection

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