Iterative roll angle estimation from dense disparity map

Abstract

The v-disparity map is predominantly used to estimate the parameters of the vertical profile of the road surface. Once the road surface is modelled, an object that lies away from it can be detected and determined as either an obstacle or a pothole. The accuracy of this estimation is largely affected by the clarity of the v-disparity map which can be vastly improved by eliminating the effect of a non-zero roll angle. With a rotation around the roll angle for the disparity map, a better v-disparity histogram can be provided. This paper presents a method for accurate roll angle estimation through analysis of the disparity and v-disparity maps. Since the quality of the v-disparity map is improved by rotating the disparity map by the estimated roll angle, this leads to improved road modelling. The more accurate the roll angle estimation, the larger this improvement is.

A Robust Roll Angle Estimation Algorithm Based on Gradient Descent

“…However, finding a proper small area for plane modeling is always challenging, because such an area may contain an obstacle [18]. This can severely affect the plane modeling precision [21]. In this regard, an iterative roll angle estimation algorithm was proposed in [21], where multiple small areas were selected for plane modeling.…”

“…This can severely affect the plane modeling precision [21]. In this regard, an iterative roll angle estimation algorithm was proposed in [21], where multiple small areas were selected for plane modeling. The roll angle was then estimated from the optimum selected area.…”

A novel disparity transformation algorithm for road segmentation

“…These algorithms generally fit a linear or quadratic road model to the best path in the v-disparity map and then analyse the difference between the actual and fitted disparity values to determine the road pixels [8]. However, the nonzero roll angle γ (see Figure 1) usually makes the best path in the v-disparity map ambiguous (see Figure 2b), which further severely affects the accuracy of the road model estimation [9]. Therefore, γ has to be estimated beforehand to address the above issue.…”

“…Over the past decade, various technologies, e.g., inertial measurement units (IMU) [10,11,12,13] and passive sensing [14,9], have been utilised to estimate γ. The methods based on IMU usually combine the data obtained from different sensors, e.g., GPS, accelerometers and gyroscopes, to provide an accurate estimation of the vehicle state [12,13].…”

“…However, the roll angle usually changes over time in actual cases, and therefore such assumptions are not always valid. In this regard, some authors resorted to 3-D information in order to estimate roll angle more accurately [15,9,17,18].…”