The use of sensors is ubiquitous in our IT-based society; smartphones, consumer electronics, wear... more The use of sensors is ubiquitous in our IT-based society; smartphones, consumer electronics, wearable devices, healthcare systems, industries, and autonomous cars, to name but a few, rely on quanti ...
This paper presents an efficient and accurate radar odometry pipeline for large-scale localizatio... more This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections perazimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a pointto-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5 ms per frame without need of environmental specific t...
We present an improved statistical model of the measurement process of triangulation Light Detect... more We present an improved statistical model of the measurement process of triangulation Light Detection and Rangings (Lidars) that takes into account bias and variance effects coming from two different sources of uncertainty: \((i) \) mechanical imperfections on the geometry and properties of their pinhole lens - CCD camera systems, and \((ii) \) inaccuracies in the measurement of the angular displacement of the sensor due to non ideal measurements from the internal encoder of the sensor. This model extends thus the one presented in [2] by adding this second source of errors. Besides proposing the statistical model, this chapter considers: \((i) \) specialized and dedicated model calibration algorithms that exploit Maximum Likelihood (ML)/Akaike Information Criterion (AIC) concepts and that use training datasets collected in a controlled setup, and \((ii) \) tailored statistical strategies that use the calibration results to statistically process the raw sensor measurements in non cont...
This paper presents a new detector for filtering noise from true detections in radar data, which ... more This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning FrequencyModulated Continuous Wave (FMCW) radars can be useful for localisation and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded FalseAlarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR to the use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-a...
We propose an expectation maximization (EM) strategy for improving the precision of time of fligh... more We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser's datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.
We consider the problem of calibrating distance measurement of Light Detection and Ranging (lidar... more We consider the problem of calibrating distance measurement of Light Detection and Ranging (lidar) sensor without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground, and so that its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. We moreover consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relativ...
The use of sensors is ubiquitous in our IT-based society; smartphones, consumer electronics, wear... more The use of sensors is ubiquitous in our IT-based society; smartphones, consumer electronics, wearable devices, healthcare systems, industries, and autonomous cars, to name but a few, rely on quanti ...
This paper presents an efficient and accurate radar odometry pipeline for large-scale localizatio... more This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections perazimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a pointto-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5 ms per frame without need of environmental specific t...
We present an improved statistical model of the measurement process of triangulation Light Detect... more We present an improved statistical model of the measurement process of triangulation Light Detection and Rangings (Lidars) that takes into account bias and variance effects coming from two different sources of uncertainty: \((i) \) mechanical imperfections on the geometry and properties of their pinhole lens - CCD camera systems, and \((ii) \) inaccuracies in the measurement of the angular displacement of the sensor due to non ideal measurements from the internal encoder of the sensor. This model extends thus the one presented in [2] by adding this second source of errors. Besides proposing the statistical model, this chapter considers: \((i) \) specialized and dedicated model calibration algorithms that exploit Maximum Likelihood (ML)/Akaike Information Criterion (AIC) concepts and that use training datasets collected in a controlled setup, and \((ii) \) tailored statistical strategies that use the calibration results to statistically process the raw sensor measurements in non cont...
This paper presents a new detector for filtering noise from true detections in radar data, which ... more This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning FrequencyModulated Continuous Wave (FMCW) radars can be useful for localisation and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded FalseAlarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR to the use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-a...
We propose an expectation maximization (EM) strategy for improving the precision of time of fligh... more We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser's datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.
We consider the problem of calibrating distance measurement of Light Detection and Ranging (lidar... more We consider the problem of calibrating distance measurement of Light Detection and Ranging (lidar) sensor without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground, and so that its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. We moreover consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relativ...
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