76 citations · 103 across the 15 of their papers we have counts for
16 papers
Mapping Tire-Road Friction Coefficients Along Driven Trajectories
Risto Ojala
Information of the tire-road friction coefficient is critical for intelligent vehicle systems, as friction establishes the limits at which the vehicle can be manoeuvred without los…
Multi-Observer Vehicle Localization Case Study with Roadside Radar and Connected Vehicle Sensing
Aleksi Pippuri, Nilusha Jayawickrama, Risto Ojala
In modern intelligent transportation systems, it is essential to accurately estimate vehicle positions, especially in mixed traffic conditions where both connected and conventional…
Decoupled Prototype Matching with Vision Foundation Models for Few-Shot Industrial Object Detection
Hari Prasanth S. M., Nilusha Jayawickrama, Risto Ojala
Industrial object detection systems typically rely on large annotated datasets, which are expensive to collect and challenging to maintain in industrial scenarios where the invento…
Person Detection and Tracking from an Overhead Crane LiDAR
Nilusha Jayawickrama, Henrik Toikka, Risto Ojala
This paper investigates person detection and tracking in an industrial indoor workspace using a LiDAR mounted on an overhead crane. The overhead viewpoint introduces a strong domai…
Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering
Hari Prasanth S. M., Pejman Habibiroudkenar, Eerik Alamikkotervo +2
Event cameras provide high-temporal-resolution visual sensing that is well suited for observing fast-moving aerial objects; however, their use for drone trajectory prediction remai…
Glass Segmentation with Fusion of Learned and General Visual Features
Risto Ojala, Tristan Ellison, Mo Chen
Glass surface segmentation from RGB images is a challenging task, with a number of applications in robotics and scene understanding. As glass lacks coherent visual characteristics,…
Automatic Labelling for Low-Light Pedestrian Detection
Dimitrios Bouzoulas, Eerik Alamikkotervo, Risto Ojala
Pedestrian detection in RGB images is a key task in pedestrian safety, as the most common sensor in autonomous vehicles and advanced driver assistance systems is the RGB camera. Lo…
Label Correction for Road Segmentation Using Road-side Cameras
Henrik Toikka, Eerik Alamikkotervo, Risto Ojala
Reliable road segmentation in all weather conditions is critical for intelligent transportation applications, autonomous vehicles and advanced driver's assistance systems. For robu…
Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions
Eerik Alamikkotervo, Henrik Toikka, Kari Tammi +1
Robust road segmentation in all road conditions is required for safe autonomous driving and advanced driver assistance systems. Supervised deep learning methods provide accurate ro…
Road Surface Friction Estimation for Winter Conditions Utilising General Visual Features
Risto Ojala, Eerik Alamikkotervo
In below freezing winter conditions, road surface friction can greatly vary based on the mixture of snow, ice, and water on the road. Friction between the road and vehicle tyres is…
DynaHull: Density-centric Dynamic Point Filtering in Point Clouds
Pejman Habibiroudkenar, Risto Ojala, Kari Tammi
In the field of indoor robotics, accurately localizing and mapping in dynamic environments using point clouds can be a challenging task due to the presence of dynamic points. These…
TADAP: Trajectory-Aided Drivable area Auto-labeling with Pre-trained self-supervised features in winter driving conditions
Eerik Alamikkotervo, Risto Ojala, Alvari Seppänen +1
Detection of the drivable area in all conditions is crucial for autonomous driving and advanced driver assistance systems. However, the amount of labeled data in adverse driving co…
Lightweight Regression Model with Prediction Interval Estimation for Computer Vision-based Winter Road Surface Condition Monitoring
Risto Ojala, Alvari Seppänen
Winter conditions pose several challenges for automated driving applications. A key challenge during winter is accurate assessment of road surface condition, as its impact on frict…
Multi-Echo Denoising in Adverse Weather
Alvari Seppänen, Risto Ojala, Kari Tammi
Adverse weather can cause noise to light detection and ranging (LiDAR) data. This is a problem since it is used in many outdoor applications, e.g. object detection and mapping. We…
4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds
Alvari Seppänen, Risto Ojala, Kari Tammi
Reliable point cloud data is essential for perception tasks \textit{e.g.} in robotics and autonomous driving applications. Adverse weather causes a specific type of noise to light…
No GPU? No problem: an ultra fast 3D detection of road users with a simple proposal generator and energy-based out-of-distribution PointNets
Alvari Seppänen, Eerik Alamikkotervo, Risto Ojala +2
This paper presents a novel architecture for point cloud road user detection, which is based on a classical point cloud proposal generator approach, that utilizes simple geometrica…