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David Wisth

6 accepted papers

2023

Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping

Lintong Zhang, Michael Helmberger, Lanke Frank Tarimo Fu, David Wisth, Marco Camurri, Davide Scaramuzza +1
RA-L 2023

Simultaneous Localization and Mapping (SLAM) is being deployed in real-world applications, however many state-of-the-art solutions still struggle in many common scenarios. A key necessity in progressing SLAM research is the availability of high-quality datasets and fair and transparent benchmarking.

Cited by 79Source ↗Scholar ↗
2022

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

Lintong Zhang, David Wisth, Marco Camurri, Maurice F. Fallon
RA-L 2022

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. We focus on motion tracking in challenging environments, such as narrow corridors, dark spaces with

Cited by 31Source ↗Scholar ↗
2021

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

David Wisth, Marco Camurri, Sandipan Das, Maurice F. Fallon
RA-L 2021

We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to ext

Cited by 114Source ↗Scholar ↗
2020

Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry

David Wisth, Marco Camurri, Maurice Fallon
ICRA 2020poster

In this paper, we present a novel factor graph formulation to estimate the pose and velocity of a quadruped robot on slippery and deformable terrain. The factor graph introduces a preintegrated velocity factor that incorporates velocity inputs from leg odometry and also estimates related biases. Fro…

Cited by 38Source ↗Scholar ↗
2020

The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth

Milad Ramezani, Yiduo Wang, Marco Camurri, David Wisth, Matias Mattamala, Maurice Fallon
IROS 2020poster

In this paper, we present a large dataset with a variety of mobile mapping sensors collected using a handheld device carried at typical walking speeds for nearly 2.2 km around New College, Oxford as well as a series of supplementary datasets with much more aggressive motion and lighting contrast. Th…

Cited by 238Source ↗Scholar ↗