Search Results for author: Jukka Heikkonen

Found 7 papers, 0 papers with code

Long-Term Autonomy in Forest Environment using Self-Corrective SLAM

no code implementations31 Dec 2020 Paavo Nevalainen, Parisa Movahedi, Jorge Peña Queralta, Tomi Westerlund, Jukka Heikkonen

The algorithm adds new iterative closest point (ICP) cases to the initial SLAM and measures the resulting map quality by the mean of the root mean squared error (RMSE) of individual tree clusters.

Simultaneous Localization and Mapping Robotics

Asynchronous Corner Tracking Algorithm based on Lifetime of Events for DAVIS Cameras

no code implementations29 Oct 2020 Sherif A. S. Mohamed, Jawad N. Yasin, Mohammad-Hashem Haghbayan, Antonio Miele, Jukka Heikkonen, Hannu Tenhunen, Juha Plosila

In this paper, a novel asynchronous corner tracking method is proposed that uses both events and intensity images captured by a DAVIS camera.

Dynamic Resource-aware Corner Detection for Bio-inspired Vision Sensors

no code implementations29 Oct 2020 Sherif A. S. Mohamed, Jawad N. Yasin, Mohammad-Hashem Haghbayan, Antonio Miele, Jukka Heikkonen, Hannu Tenhunen, Juha Plosila

The algorithm is based on an events' filtering strategy whose purpose is 1) to increase the accuracy by deliberately eliminating some incoming events, i. e., noise, and 2) to improve the real-time performance of the system, i. e., preserving a constant throughput in terms of input events per second, by discarding unnecessary events with a limited accuracy loss.

Object Tracking

Utilizing remote sensing data in forest inventory sampling via Bayesian optimization

no code implementations17 Sep 2020 Jonne Pohjankukka, Sakari Tuominen, Jukka Heikkonen

In addition, it is also reasonable to study the utilization of RS data in inventory sampling, which can further improve the estimation of forest variables.

Bayesian Optimization

Estimating the Prediction Performance of Spatial Models via Spatial k-Fold Cross Validation

no code implementations28 May 2020 Jonne Pohjankukka, Tapio Pahikkala, Paavo Nevalainen, Jukka Heikkonen

To overcome this problem we propose a modified version of the CV method called spatial k-fold cross validation (SKCV), which provides a useful estimate for model prediction performance without optimistic bias due to SAC.

Playtime Measurement with Survival Analysis

no code implementations4 Jan 2017 Markus Viljanen, Antti Airola, Jukka Heikkonen, Tapio Pahikkala

Throughout this paper, we illustrate the application of these methods to real world game development problems on the Hipster Sheep mobile game.

Survival Analysis

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